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STAT 2150 > PRELIMS > Flashcards

PRELIMS Flashcards

1
Q

is a spreadsheet application which features calculation, graphing tools, pivot tables and a macro programming language called VBA (Visual Basic for Applications)

A

Microsoft Excel

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2
Q

True or False:
Excel cannot think, it ONLY computes what you command them to do

A

TRUE

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3
Q

PARTS OF EXCEL:
-used to open new or saved workbooks, save, print or close workbooks or manage Excel options

A

File Menu/Tab

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4
Q

PARTS OF EXCEL:
-located at the upper left corner of the screen wherein you can place the most commonly used commands here. It is customizable.

A

Quick Access Toolbar

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5
Q

PARTS OF EXCEL:
-requires internet connection to provide answers to inquiries

A

Excel Help Function

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6
Q

PARTS OF EXCEL:
-enter and edit values, formulas, and text

A

Formula Bar

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7
Q

PARTS OF EXCEL:
-consists of rows and columns that intersect to form cells

A

Worksheet

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8
Q

PARTS OF EXCEL:
-it provides information for the active worksheet. Switch views options or zoom in/out

A

Status Bar

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9
Q

Core document of Excel. It can hold any number of worksheets/spreadsheets

A

Workbook

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10
Q

manages how Excel will display and interpret data in the cells.

A

data type

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11
Q

Most commonly data types in excel:

A
  1. Number
  2. Percentage
  3. Text
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12
Q

Data type:
-cells that contain only numerals, commas and decimal points that can be used in numerical calculations.

A

Number

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13
Q

Data type:
-multiplies the cell value by 100 and displays the value with a %.

A

Percentage

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14
Q

Data type:
-cells that contain letters, numbers, spaces, or any other keyboard character.

A

Text

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15
Q

This is used to keep titles in sight when you scroll down a page.

A

Freeze Panes

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16
Q
  • real power of an Excel spreadsheet
  • uses standard mathematical symbols to operate on cell addresses and/or numbers
  • can be a combination of values (numbers or cell references) and mathematical operators into an algebraic expression
A

Formulas and Functions

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17
Q

Excel requires this in EVERY formula to begin with

A

equal sign (=)

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18
Q

a predefined/prewritten formula that takes a value or values, performs an operation on a range of cells you select, and returns a value or values.

A

Function

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19
Q
  • Excel’s very useful and powerful feature
  • can be used to summarize, analyze, explore and present your data with ease
A

Pivot Tables

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20
Q

outcomes of the variables are expressed non-numerically or categorically

A

Qualitative variable/dataset

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21
Q
  • a tabular summary of data showing the number (frequency) of items in each of several nonoverlapping classes
  • organization of data in tabular form, using classes (or intervals) and frequencies
A

Frequency Distribution Table

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22
Q

number of times the value occurs in the data set

A

Frequency

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23
Q

– represent data that can be placed in specific categories, such as gender, hair color, or religious affiliation

A

Qualitative FDT (Frequency Distribution Table)

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24
Q

Qualitative dataset can be presented through :

A
  1. Qualitative FDT
  2. Graphical presentation such as:
    a. Bar Graph
    b. Pie Graph
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25
Q

consists of a series of rectangular bars where the length of the bar represents the quantity or frequency for each category if the bars are arranged horizontally.

A

Bar Graph

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26
Q

a circular graph that is useful in showing how a total quantity is distributed among a group of categories

A

Pie Graph

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27
Q

outcomes of the variables are expressed numerically that are meaningful or indicate some sort of amount

A

Quantitative variable/dataset

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28
Q

Quantitative dataset can be presented through:

A

a. Numerical summary measures
b. Graphical presentations such as:
1. Histogram
2. Box plot

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29
Q

Measures of Location:

A
  1. Percentiles
  2. Deciles
  3. Quartiles
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30
Q
  • A number that is meant to convey the idea of ‘centralness’ for the data set
  • A value about which the set of observations tend to cluster
  • Typical/average value of the data set
A

Measures of Central Tendency

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31
Q

3 measures of central tendency

A

1.Mean
2.Median
3.Mode

32
Q

a quantity that measures the spread or variability of the observation in a given population.

A

Measures of Variability

33
Q

Common measures of variability

A
  1. Range
  2. Variance
  3. Standard deviation
  4. Standard error of the mean
  5. Coefficient of variation
34
Q

the mean of the squared deviations of the observation from the mean

A

Variance

35
Q

The average deviation between the individual scores in the distribution and the mean for the distribution; square root of the variance

A

Standard Deviation

36
Q

standard deviation of the sampling distribution of the mean

A

Standard Error of the mean

37
Q
  • defined as the ratio of the standard deviation and the mean and is expresses in percent
  • unitless; useful for comparing two data sets with different units of measurement
A

Coefficient of Variation (CV)

38
Q

graphical representation of data especially useful for showing trends over a period of time

A

Line Chart

39
Q

can be used to test whether data is normally distributed. This test simply consists of looking at it and discerning whether it approximates the bell curve of a normal distribution.

A

Histogram

40
Q

is an alternative graphical method of assessing normality to the histogram and is easier to use when there are small sample sizes. The scatter should lie as close to the line as possible with no obvious pattern coming away from the line for the data to be considered normally distributed.

A

Normal Q-Q plot

41
Q

can’t actually be used to test for normality, they can be useful for testing for symmetry, which often is a sufficient substitute for normality

A

Box plots

42
Q

a measure of the asymmetry of the probability distribution of a random variable about its mean.

A

Skewness

43
Q

If skewness is 0, the data are

A

perfectly symmetrical

44
Q

If skewness is between -0.5 and 0.5, the distribution is

A

approximately symmetric.

45
Q

If skewness is between -1 and -0.5 or between 0.5 and 1, the distribution is

A

moderately skewed

46
Q

If skewness is less than -1 or greater than 1, the distribution is

A

highly skewed

47
Q

tells you the height and sharpness of the central peak, relative to that of a standard bell curve.

A

Kurtosis

48
Q

A perfect normal distribution has a kurtosis of 0. Also called as

A

mesokurtic distribution

49
Q

Kurtosis value above 0(sharper peak and longer/flatter tails)

A

Leptokurtic Distribution

50
Q

Kurtosis value below 0 (rounder peak and shorter/thinner tails)

A

Platykurtic distribution

51
Q

often called distribution free tests and can be used instead of their parametric equivalent.

A

Non-parametric tests

52
Q

What are the two options if the data is not normally distributed?

A
  1. Transform the dependent variable
  2. Use a non-parametric test
53
Q

claim or statement about a property of a population.

A

Hypothesis

54
Q

Types of Error in Hypothesis Testing:
– mistake of rejecting Ho when it is true.
α = P (Type ? error) = P(reject Ho / Ho is true)

A

Type I error

55
Q

Types of Error in Hypothesis Testing:
mistake of failing to reject he Ho, when it is false.
β = P(Type ? error) = P(accept Ho / Ho is false)

A

Type II error

56
Q
  • A number that is meant to convey the idea of ‘centralness’ for the data set
  • A value about which the set of observations tend to cluster
  • Typical/average value of the data set
A

Measures of Central Tendency

57
Q

-sample statistic or a value based on the sample data.

-used in making decision about the rejection of the null hypothesis

A

Test Statistic

58
Q

set of all values of the test statistic that would case to reject the Ho

A

Critical Region

59
Q

value that separates the critical region from the values of the test statistic that would lead to the rejection of Ho

A

Critical Value

60
Q

-(also called crosstabs) are useful as a rudimentary tool to analyze the relationship between two variables

-one variable is presented in the columns and the other in the rows.

-most useful when variables have a limited number of response categories.

A

Contingency Tables

61
Q

The probability of observing a sample value as extreme as, or more extreme than, the value observed, given that the null hypothesis is true.

A

P-value

62
Q

Used to compare the mean value of a sample with a constant value

A

t-Test for One Sample Mean

63
Q
  • It is used when researchers wish to compare two sample means, using experimental and control groups.
  • Main interest is on the difference between the two populations.
A

Hypothesis Testing for Two Populations

64
Q

Main interest in on the difference between mu1 mu2

A

z-Test for Two Sample Means

65
Q

the type of test where samples are independent if the sample selected from one population is not related to the sample selected from the other population.

A

t-Test for Two Independent Sample Means

66
Q

-the type of Test one sample is related to the other sample.

-such samples are referred to as paired sample or matched sample

A

t-Test for Two Dependent Sample Means

67
Q

It is used to test whether the variances of two populations are equal.

A

F-test for Two-Sample Variance

68
Q

allows you to compare the means of three or more independent samples.

It is suitable when the values are drawn from a normal distribution and when the variance is approximately the same in each group.

A

Analysis of Variance (ANOVA)

69
Q

an extension of testing the difference between two population means

we are testing the Ho that several population means are all equal against the Ha that at least two of these population means are not equal

A

Completely Randomized Design(CRD)

70
Q

The whole of the eu’s are first subdivided into a number of blocks or strata in such a way that the eu’s within each block are made more homogeneous compared to eu’s between blocks.

The number of replications per treatment is equal to the number of blocks. Blocks are groups of eu’s affected by a common level of a confounding variable.

A

Randomized Complete Block Design (RCBD)

71
Q

refers to the grouping of a number of eu’s that are relatively homogeneous. Proper blocking produces large differences among blocks, leaving eu’s within a block as homogeneous as possible

A

Blocking

72
Q

a statistical technique to determine the strength or degree of linear relationship existing between two variables.

A

Linear Correlation Analysis

73
Q

A measure of the degree of linear relationship

A

population coefficient p

74
Q

is a type of mathematical diagram using Cartesian coordinates to display values for two variables for a set of data

A

Scatter plot/Scatter Diagram

75
Q
  • Used to predict the value of a dependent variable based on the value of at least one independent variable
  • Used to explain the impact of changes in an independent variable on the dependent variable.
A

Simple Linear Regression Analysis

STAT 2150 (1 decks)

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