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STAT 121 > Final: Glossary > Flashcards

Final: Glossary Flashcards

1
Q

alternative hypothesis

A

alternative hypothesis: A statement about the value of a parameter that is either “less than,” “greater than,” or “not equal to” a hypothesized number or another parameter; the hypothesis that the researcher usually wants to prove or verify.

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2
Q

Analysis of Variance (ANOVA)

A

Analysis of Variance (ANOVA): A procedure used to test equality of three or more means.

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3
Q

association

A

association: For quantitative data, large values of one variable tend to occur with large (or small) values of another variable. For categorical data, certain responses for one variable tend to occur with certain responses of the other variable.

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4
Q

association vs. causation

A

association vs. causation: We can only argue causation from association if the results having significant association are from an experiment.

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5
Q

bar graph

A

bar graph: A graphical representation of categorical data. Names of each category are listed on the x axis and a bar that has height representing the frequency (or percentage) in that category is placed over each category name.

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6
Q

bias (sampling)

A

bias (sampling): A condition that occurs when the design of a study systematically favors certain outcomes.

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7
Q

bivariate data

A

bivariate data: Two measurements are made on each unit.

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8
Q

block

A

block: A group of experimental units sharing some common characteristic. In a randomized complete block design, random allocation of treatments is carried out separately within each group.

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9
Q

boxplot

A

boxplot: A plot of data that incorporates the maximum observation, the minimum observation, the first quartile, the second quartile (median), and the third quartile.

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10
Q

categorical (or qualitative) variable

A

categorical (or qualitative) variable: A variable that can be classified into groups or categories such as gender and religion.

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11
Q

causation

A

causation: Changes in the explanatory variable directly affect the response variable. Experiments are needed to verify causation.

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12
Q

census

A

census: The enumeration of every unit in a population.

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13
Q

center

A

center: A summary number about which observations tend to cluster. Measures of center include the mean and
the median.

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14
Q

center line

A

center line: The middle line on a control chart. Its value is the target value of the mean when the process is in control.

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15
Q

Central Limit Theorem (CLT):

A

Central Limit Theorem (CLT): The name of the theorem stating that the sampling distribution of a statistic (e.g. x ) is approximately normal whenever the sample is large and random.

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16
Q

Chi-distribution

A

Chi-distribution: The theoretical distribution that models the test statistic for doing Chi-Square tests.

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17
Q

Chi-square test statistic

A

Chi-square test statistic: A test statistic computed from data that has an approximate Chi-square distribution.

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18
Q

claimed parameter value

A

claimed parameter value: The value of the parameter as given in the null hypothesis.

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19
Q

comparison study

A

comparison study: A study that compares only active treatments to determine which works best.

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20
Q

conditions

A

conditions: The basic premises that must be checked before using a statistical procedure.

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21
Q

conditional distribution

A

conditional distribution: The distribution of one variable restricted to a single row (or column) of another variable in a two way table. A conditional distribution is found by dividing the values in the row (or column) by the row (or column) total.

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22
Q

conditional percentage

A

conditional percentage: In a contingency table, the percentage of a category in a row (or column) found by dividing the appropriate cell count by the row (or column) total.

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23
Q

confidence interval

A

confidence interval: An estimate of the value of a parameter in interval form with an associated level of confidence; it gives a list of plausible values for the parameter based on the value of the statistic.

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24
Q

confidence level

A

confidence level: The percentage of all possible samples for which the confidence intervals will contain the parameter being estimated; selected subjectively by the researcher.

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25
Q

confounding

A

confounding: A situation where the effect of one variable on the response variable cannot be separated from the effect of another variable on the response variable.

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26
Q

control treatment

A

control treatment: A treatment where no experimental condition is applied to the units in order to determine whether the active treatments affect the response. This enables the researcher to “control” for lurking variables.

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27
Q

control chart

A

control chart: A chart plotting the means ( x ’s) of regular samples of size n against time. It has a center line and upper and lower control limits to determine whether a process is in control or out of control.

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28
Q

control limits

A

control limits: Lines on either side of the center line computed using μ − 3σ /sqrt(n) and μ + 3σ/sqrt(n) . A sample mean outside of these bounds signals that the process is out of control.

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29
Q

convenience sample

A

convenience sample: A sample type where the researcher contacts those subjects who are readily available and does not use any random selection. The results are almost always biased.

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30
Q

correlation coefficient

A

correlation coefficient: A measure of the strength of the linear relationship between two quantitative variables, symbolized with the letter r.

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31
Q

data

A

data: Information collected on individuals.

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32
Q

degrees of freedom

A

degrees of freedom: A characteristic of the t-distribution (and other distributions like F and χ2) indicating the amount of information available in the data. A complete definition of “degrees of freedom” is beyond the scope of an introductory statistics course.

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33
Q

density curve

A

density curve: A mathematical model used to describe the overall pattern of the distribution of a random variable

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34
Q

deviation

A

deviation: The difference (distance) between an observation and the mean of all the observations in a data set, or the difference between an observation and the corresponding regression model estimate.

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35
Q

direction of relationship

A

direction of relationship: A characteristic of data in a scatterplot that is identified as either a positive or negative association

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36
Q

distribution

A

distribution: A list of all possible values of a variable together with the frequency (or probability) of each value.

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37
Q

dotplot

A

dotplot: A one dimensional plot of a quantitative data set where each value in the data set is represented by a dot
above its corresponding location on the x axis.

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38
Q

double blind study

A

double blind study: An experiment where neither the subjects nor the diagnosticians (e.g. doctor or nurse) know which treatment is administered to whom.

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39
Q

equal variance or equal standard deviation)

A

equal variance or equal standard deviation): Variances (or standard deviations) for each of the treatment groups (or samples) in ANOVA are all equal. In regression, the variances of the y’s at each x are all assumed to be equal.

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40
Q

estimate of a parameter

A

estimate of a parameter: A single value or a range of values used to estimate a parameter.

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41
Q

expected count

A

expected count: An estimate of how many observations should be in a cell of a two way table if there were no
association between the row and column variables.

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42
Q

experiment

A

experiment: A study where treatments are deliberately imposed on the individuals in the study before data is gathered in order to observe their responses to the treatment.

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43
Q

explained variation

A

explained variation: The amount of total variation in the y’s that is accounted for by a regression model; it is equalto∑(yˆ−y)2 .

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44
Q

explanatory variable

A

explanatory variable: A variable that may or may not explain the outcomes (responses) of a study, also called independent or predictor variable.

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45
Q

extrapolation

A

extrapolation: Using a model to predict a y value for an x value that is outside the range of observed x’s. Extrapolation is dangerous and strongly discouraged because the relationship between x and y may be different outside the range of observed x’s.

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46
Q

factor

A

factor: A term synonymous with explanatory variable in an experiment.

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47
Q

fail to reject Ho

A

fail to reject Ho: The appropriate statistical conclusion in hypothesis testing when the P-value is greater
than α; equivalently, conclude that “There is not enough evidence to believe Ha.”

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48
Q

failure

A

failure: Any category that is not of primary interest in a categorical data set.

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49
Q

F distribution

A

F distribution: The distribution that models the ratio of two variance estimates; used in ANOVA for obtaining the P-value for testing equality of three or more means.

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50
Q

five number summary

A

five number summary: These five values: minimum, Q1, median, Q3, maximum; preferred numerical summary when data are very skewed or outliers are present.

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51
Q

follow-up analysis

A

follow-up analysis: The analysis performed on data after an overall test on the equality of multiple means or the equality of multiple proportions is found to be significant. It determines which means or which proportions differ from which.

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52
Q

form of relationship

A

form of relationship: A description of data in a scatterplot indicating whether the data have a linear relationship, a curved relationship or no relationship.

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53
Q

F test statistic

A

F test statistic: A test statistic that has an F distribution.

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54
Q

histogram

A

histogram: A graphical display of a quantitative data set; data are grouped into intervals (usually of equal width) and a bar is drawn over each interval having height proportional to the frequency (or percentage) of values in the interval. Values of the variable are given on the x axis and frequencies (or percentages) are given on the y axis. Histograms are examined to determine shape, center and spread.

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55
Q

in control

A

in control: A process functioning within acceptable limits.

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56
Q

independent samples

A

independent samples: SRS’s collected separately from each of two (or more) disjoint populations; matched pairs
data are considered to be dependent samples.

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57
Q

individual

A

individual: Each object or unit described or examined in a data set.

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58
Q

inference

A

inference: Using results from a sample statistic value to draw conclusions about the population parameter.

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59
Q

influential point

A

influential point: An observation that substantially alters the fitted regression equation.

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60
Q

interquartile range (IQR)

A

interquartile range (IQR): The difference between Q3 and Q1 (i.e. Q3 – Q1); the length of the box in a boxplot; contains 50% of the data.

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61
Q

interviewer bias

A

interviewer bias: Bias introduced into survey results by body language, voice intonation, gender, race, etc. of an interviewer.

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62
Q

lack of realism

A

lack of realism: A weakness in experiments where the setting of the experiment does not realistically duplicate the conditions we really want to study.

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63
Q

law of large numbers

A

law of large numbers: The fact that the average of observed values in a sample ( x ) will tend to get closer and closer to μ as the sample size increases.

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64
Q

least squares regression line

A

least squares regression line: The line that minimizes the sum of squared residuals.

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65
Q

left skewed

A

left skewed: A density curve where the left side of the distribution extends in a long tail. (Mean < median.)

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66
Q

left-tailed alternative hypothesis

A

left-tailed alternative hypothesis: An alternative hypothesis that states the parameter value is less than some number or the parameter from another treatment or population. (e.g. H a : μ < 85 )

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67
Q

lower tailed alternative hypothesis

A

lower tailed alternative hypothesis: Another name for a left-tailed alternative hypothesis.

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68
Q

lurking variable

A

lurking variable: A variable that the researcher is not necessarily interested in studying but which affects the relationship between the explanatory variable and the response variable.

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69
Q

mall-intercept sample

A

mall-intercept sample: A sample where respondents are contacted in a shopping mall or similar location. Often the method of selection is haphazard although occasionally systematic.

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70
Q

margin of error for 95% confidence

A

margin of error for 95% confidence: The maximum amount that a statistic value will differ from the parameter value for the middle 95% of the statistics. (Note: Changing the level of confidence changes the percentage of interest, e.g. 95%.)

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71
Q

marginal distribution

A

marginal distribution: The distribution of only one variable in a two way table using counts found by summing over the categories of the other variable.

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72
Q

marginal percentage

A

marginal percentage: The percentage for a row (or column) total in a two table found by dividing the row (or column) total by the table total.

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73
Q

matched pairs

A

matched pairs: A design of experiment that combines matching of subject or measurements with randomization. Either two measurements taken on each unit (such as pre and post) OR measurements taken on two individuals matched by some characteristics different from the explanatory variable and the response variable.

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74
Q

matched pairs t procedure for mean

A

matched pairs t procedure for mean: The hypothesis testing method for matched pairs data. The standard null hypothesis is H0: μd = 0 where μd is the mean difference between treatments.

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75
Q

maximum

A

maximum: The largest value in a data set.

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76
Q

mean

A

mean: A measure of the center of the data; a value that “balances” the data; found by summing all the data and
dividing by the number of data points.

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77
Q

measurement

A

measurement: A recorded fact about an individual; may be either numerical (quantitative) or qualitative (categorical).

78
Q

measurement bias

A

measurement bias: Bias introduced into survey results because of poorly worded questions, interviewer effects, measuring instrument difficulties, etc.

79
Q

measurement variation

A

measurement variation: Differences in repeated measurements on the same object.

80
Q

median

A

median: A measure of the center of data; a value that splits the data in half; the “middle” number after the data
have been sorted.

81
Q

minimum

A

minimum: The smallest value in a data set.

82
Q

multiple analyses (i.e., multiple comparisons)

A

multiple analyses (i.e., multiple comparisons): Performing two or more tests of significance on the same data set. This inflates the overall α (probability of a type I error) for the tests (i.e., the more analyses performed, the greater the chance of falsely rejecting at least one true null hypothesis).

83
Q

multi-stage sample

A

multi-stage sample: A type of sample from a population that has groups and sub-groups. First, some groups are randomly selected, and then some sub-groups from within the selected groups are randomly sampled. Finally, individuals are randomly selected from within the sampled sub-groups. This can be extended to sub-sub-groups, etc.

84
Q

natural variation

A

natural variation: Variation from object to object within a population.

85
Q

non-probability sample

A

non-probability sample: A sample selected without randomization; hence, the probability of obtaining a
particular sample cannot be computed.

86
Q

non-response bias

A

Bias introduced into survey results because individuals refuse to participate.

87
Q

Normal distribution:

A

A bell-shaped, symmetric density curve that is often used as a model for data or other random variables; specified by μ and σ.

88
Q

normality of Y at each X:

A

The distribution of all the Y values at each possible value of X is normal.

89
Q

null hypothesis:

A

The hypothesis that the researcher assumes to be true until sample results indicate otherwise; the
hypothesis of no difference or no change; usually the hypothesis that the researcher wants to disprove.

90
Q

observational study:

A

A study that merely observes conditions of individuals in a population and records information; the population is disturbed as little as possible. (Note: treatments are not imposed on individuals nor are individuals randomly assigned to treatment groups.)

91
Q

observed count:

A

The count of individuals actually observed in a given cell of a two-way table.

92
Q

observed statistic

A

: The value of the statistic computed from the data.

93
Q

one sample t procedure for means:

A

An inferential procedure using the mean from one sample to test or estimate the population mean; the test statistic follows a t distribution; used when σ is unknown.

93
Q

one sample t procedure for means:

A

An inferential procedure using the mean from one sample to test or estimate the population mean; the test statistic follows a t distribution; used when σ is unknown.

94
Q

one sample z procedure for proportions:

A

An inferential procedure using the proportion from one sample to test or estimate the population proportion; the approximate distribution of the test statistic is z or standard normal.

95
Q

one-sided or one-tailed test:

A

A alternative hypothesis where the researcher is interested in deviations in only one direction (“” is in Ha).

95
Q

one-sided or one-tailed test:

A

A alternative hypothesis where the researcher is interested in deviations in only one direction (“” is in Ha).

96
Q

out of control:

A

A process no longer functioning within accepted limits.

97
Q

out of control signal:

A

One sample mean outside the control limits (three standard deviations from x ) or nine
sample means in a row above (or below) the center line in a control chart.

98
Q

overall type I error rate:

A

The probability of falsely rejecting at least one true null hypothesis when multiple tests are being performed.

98
Q

overall type I error rate:

A

The probability of falsely rejecting at least one true null hypothesis when multiple tests are being performed.

99
Q

outlier:

A

An observation that falls outside the pattern of the data set. Outliers inflate the mean and often prevent us from using statistical procedures like the one sample t.

100
Q

parameter

A

parameter: A characteristic of a population that is usually unknown; this could be the mean, median, proportion, or standard deviation computed on all the data from the population. A parameter does not have variability.

101
Q

pie chart

A

pie chart: A graphical display of categorical data using a “pie”; each category is represented as a slice where the size of the slice is proportional to the percentage of data in that category.

102
Q

placebo:

A

A fake imitation treatment that resembles the real treatment in all respects except for the active ingredient.

102
Q

placebo:

A

A fake imitation treatment that resembles the real treatment in all respects except for the active ingredient.

103
Q

placebo effect

A

placebo effect: The response of patients to a treatment even though it has no active ingredient.

104
Q

“playing the game”:

A

“playing the game”: Simulating a game or process to estimate probabilities of possible outcomes.

104
Q

“playing the game”:

A

“playing the game”: Simulating a game or process to estimate probabilities of possible outcomes.

105
Q

pooled sample proportion:

A

pooled sample proportion: The value used for pˆ when computing the standard deviation: sqrt( pˆ (1 − pˆ ) * (1/n1 + 1/n2) ) for the two sample proportion z test statistic. To compute, add the number of successes in both samples and divide by the sum of the
two sample sizes.

pooled sample proportion: Overall count

EX: BYU : 55/100 and UVU: 34/100

Pooled = 84/200

106
Q

population

A

population: The entire group of individuals of interest in a study.

106
Q

population

A

population: The entire group of individuals of interest in a study.

107
Q

population distribution

A

population distribution: The distribution of a variable of interest in a population.

107
Q

population distribution

A

population distribution: The distribution of a variable of interest in a population.

108
Q

population mean (μ)

A

population mean (μ): Mean of all the observations in the population.

108
Q

population mean (μ)

A

population mean (μ): Mean of all the observations in the population.

109
Q

population proportion ( p ):

A

Proportion (or percentage) of all the observations in the population having a certain characteristic.

109
Q

population proportion ( p ):

A

Proportion (or percentage) of all the observations in the population having a certain characteristic.

110
Q

population standard deviation (σ)

A

population standard deviation (σ): The standard deviation of all observations in a population; a measure of the variability of all the population values about their mean.

110
Q

population standard deviation (σ)

A

population standard deviation (σ): The standard deviation of all observations in a population; a measure of the variability of all the population values about their mean.

111
Q

positive correlation:

A

Large values of one variable tend to occur with large values of another variable and small values of one variable tend to occur with small values of the other.

111
Q

positive correlation:

A

Large values of one variable tend to occur with large values of another variable and small values of one variable tend to occur with small values of the other.

112
Q

power (1 - B):

A

The probability of making a correct decision by rejecting a false null hypothesis; increases when α increases, or when n increases

112
Q

power (1 - B):

A

The probability of making a correct decision by rejecting a false null hypothesis; increases when α increases, or when n increases

113
Q

practical importance

A

: The difference between the observed statistic and the claimed parameter value is large enough to be worth reporting. To assess practical significance, look at the numerator of the test statistic and ask “Is the difference important?” If yes, then results are also of practical significance.
(Note: Do not assess practical significance unless results are statistically significant.)

114
Q

practical importance

A

: The difference between the observed statistic and the claimed parameter value is large enough to be worth reporting. To assess practical significance, look at the numerator of the test statistic and ask “Is the difference important?” If yes, then results are also of practical significance.
(Note: Do not assess practical significance unless results are statistically significant.)

115
Q

predicted y (symbolized by yˆ )

A

predicted y (symbolized by yˆ ): Value for y at a specified x as predicted by the regression equation; computed by plugging the value for x into the equation and solving for y.

115
Q

predicted y (symbolized by yˆ )

A

predicted y (symbolized by yˆ ): Value for y at a specified x as predicted by the regression equation; computed by plugging the value for x into the equation and solving for y.

116
Q

prediction

A

prediction: Using a regression equation to estimate a value of the response variable for a given value of the explanatory variable.

117
Q

prediction

A

prediction: Using a regression equation to estimate a value of the response variable for a given value of the explanatory variable.

118
Q

prediction interval

A

prediction interval: an interval estimate of plausible values for a single observation of Y at a specified value of X.

119
Q

probability:

A

A measure of the proportion of times an outcome occurs in a very long series of repetitions, indicating the likelihood of the outcome.

119
Q

probability:

A

A measure of the proportion of times an outcome occurs in a very long series of repetitions, indicating the likelihood of the outcome.

120
Q

probability sample:

A

probability sample: A sample chosen using some type of random device. The probability of any specific sample can be computed and is greater than zero.

121
Q

probability sample:

A

probability sample: A sample chosen using some type of random device. The probability of any specific sample can be computed and is greater than zero.

122
Q

proportion

A

proportion: The fraction of successes in either a sample ( pˆ ) or a population (p).

122
Q

proportion

A

proportion: The fraction of successes in either a sample ( pˆ ) or a population (p).

123
Q

P-value

A

P-value: The probability of getting a test statistic as extreme as or more extreme than the value actually observed
assuming H0 is true.

124
Q

Ql (First Quantile):

A

Ql (First Quantile): A location measure of the data that has approximately one-fourth or 25% of the data below it.

124
Q

Ql (First Quantile):

A

Ql (First Quantile): A location measure of the data that has approximately one-fourth or 25% of the data below it.

125
Q

Q3 (Third Quantile)

A

Q3 (Third Quantile): A location measure of the data that has approximately three-fourths or 75% of the data below it.

125
Q

Q3 (Third Quantile)

A

Q3 (Third Quantile): A location measure of the data that has approximately three-fourths or 75% of the data below it.

126
Q

quantitative variable

A

quantitative variable: A variable with numerical values such as height or weight. This type of data required for both variables in regression analysis.

127
Q

quantitative bivariate data:

A

quantitative bivariate data: The type of data required for regression analysis where two quantitative variables are measured on each individual.

128
Q

quantitative bivariate data:

A

quantitative bivariate data: The type of data required for regression analysis where two quantitative variables are measured on each individual.

129
Q

quartile

A

quartile: One of the three values that divide the ordered data set into quarters.

130
Q

quartile

A

quartile: One of the three values that divide the ordered data set into quarters.

131
Q

question wording bias:

A

question wording bias: Sample results that differ from the truth because of the wording of the question used to
obtain the information.

131
Q

question wording bias:

A

question wording bias: Sample results that differ from the truth because of the wording of the question used to
obtain the information.

132
Q

quota sample

A

quota sample: A sample selected to fill quotas for different population characteristics like gender, race, age, etc.

132
Q

quota sample

A

quota sample: A sample selected to fill quotas for different population characteristics like gender, race, age, etc.

133
Q

r^2

A

r 2 : The percentage of total variation in y, the response variable, that is accounted for by the regression of y on x (or is explained by the explanatory variable)

133
Q

r^2

A

r 2 : The percentage of total variation in y, the response variable, that is accounted for by the regression of y on x (or is explained by the explanatory variable)

134
Q

r x c table:

A

A two-way table with r rows and c columns.

134
Q

r x c table:

A

A two-way table with r rows and c columns.

135
Q

randomization

A

randomization: A method of assigning experimental units to treatment groups that eliminates bias and gives each
unit the same probability of being assigned to any treatment group.

Purpose:
To eliminate bias associated with lurking variables.

136
Q

randomized block design (RBD)

A

randomized block design (RBD): An experimental design where treatments are randomly allocated within each block.

137
Q

randomized controlled experiment (RCE):

A

An experimental design where all experimental units are assigned at random to treatments.

138
Q

random outcome:

A

An individual outcome from a random phenomenon.

138
Q

random outcome:

A

An individual outcome from a random phenomenon.

139
Q

random phenomenon

A

A phenomenon with outcomes that are individually unpredictable, but follow a
predictable distribution in the long run (i.e. in a very large number of repetitions).

140
Q

range

A

range: The maximum observation minus the minimum observation.

140
Q

range

A

range: The maximum observation minus the minimum observation.

141
Q

regression

A

regression: The mathematical modeling of relationships between numerical variables.

141
Q

regression

A

regression: The mathematical modeling of relationships between numerical variables.

142
Q

regression equation

A

regression equation: A mathematical formula for a straight line that models a linear relationship between two quantitative variables. ( yˆ = a + bx)

142
Q

regression equation

A

regression equation: A mathematical formula for a straight line that models a linear relationship between two quantitative variables. ( yˆ = a + bx)

143
Q

reject H0

A

reject H0: The appropriate statistical conclusion when the P-value is less than or equal to α; conclude that “There is enough evidence to believe Ha.”

143
Q

reject H0

A

reject H0: The appropriate statistical conclusion when the P-value is less than or equal to α; conclude that “There is enough evidence to believe Ha.”

144
Q

replication

A

replication: Having more than one individual per treatment in an experiment. (Note: Replication is NOT same as reproducibility of results or repetition of an experiment.)

144
Q

replication

A

replication: Having more than one individual per treatment in an experiment. (Note: Replication is NOT same as reproducibility of results or repetition of an experiment.)

145
Q

residual (y - yˆ ):

A

The difference between the actual y and the predicted y.

145
Q

residual (y - yˆ ):

A

The difference between the actual y and the predicted y.

146
Q

residual plot:

A

A diagnostic plot of the residuals versus the explanatory variable used to assess how well the regression line fits the data; complete scatter with a shoe box shape is good; curvature indicates that a non-linear model would better fit the data, and a megaphone pattern indicates the standard deviation of y is not the same for all values of x.

146
Q

residual plot:

A

A diagnostic plot of the residuals versus the explanatory variable used to assess how well the regression line fits the data; complete scatter with a shoe box shape is good; curvature indicates that a non-linear model would better fit the data, and a megaphone pattern indicates the standard deviation of y is not the same for all values of x.

147
Q

resistant measure:

A

resistant measure: A summary number that is not affected by outliers. The median is a resistant measure of center.

147
Q

resistant measure:

A

resistant measure: A summary number that is not affected by outliers. The median is a resistant measure of center.

148
Q

respondent bias:

A

respondent bias: Bias resulting from respondents lying when asked about illegal or unpopular behavior, forgetting or confusing past behavior, having no knowledge about the question content and not wanting to appear stupid, etc.

148
Q

respondent bias:

A

respondent bias: Bias resulting from respondents lying when asked about illegal or unpopular behavior, forgetting or confusing past behavior, having no knowledge about the question content and not wanting to appear stupid, etc.

149
Q

response bias

A

response bias: Bias resulting from how respondents answer the question (see respondent bias), or how interviewers ask the question (see interviewer bias).

149
Q

response bias

A

response bias: Bias resulting from how respondents answer the question (see respondent bias), or how interviewers ask the question (see interviewer bias).

150
Q

response variable

A

response variable: A variable that gives the outcomes of interest of the study (may not be a number); also called the dependent variable.

150
Q

response variable

A

response variable: A variable that gives the outcomes of interest of the study (may not be a number); also called the dependent variable.

151
Q

right skewed distribution:

A

right skewed distribution: A density curve where the right side of the distribution extends in a long tail; (mean> median).

151
Q

right skewed distribution:

A

right skewed distribution: A density curve where the right side of the distribution extends in a long tail; (mean> median).

152
Q

right-tailed alternative hypothesis

A

right-tailed alternative hypothesis: An alternative hypothesis that states the parameter value of a treatment or population is greater than some number or the parameter from another treatment or population (e.g., H a : μ > 85) .

152
Q

right-tailed alternative hypothesis

A

right-tailed alternative hypothesis: An alternative hypothesis that states the parameter value of a treatment or population is greater than some number or the parameter from another treatment or population (e.g., H a : μ > 85) .

153
Q

sample

A

sample: The subset of the population (individuals) that we actually examine and measure.

154
Q

sample mean (x)

A

sample mean (x) : Average of data in a sample.

155
Q
A

sample proportion ( pˆ ) : Proportion (or percentage) of successes in a sample; the number of individuals in a sample with a certain characteristic, divided by the sample size.

STAT 121 (6 decks)

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