Chapter 2-Summarizing Data

Question 1

Non linear

Answer 1

Plots that have not visible sign of curvature or straight lines in the data points

Question 2

Scatter plot

Answer 2

A plot that shows each data point in an experiment

Question 3

Dot plot

Answer 3

A one variable scatter plot

Question 4

Mean

Answer 4

Also commonly referred to as the average. It is a way to measure the distribution or spread of the data. Compute by adding up all the individual data points and dividing by the number of observations.

Question 5

Histogram

Answer 5

A type of plot that groups the data into categories and shows frequency within the categories. Provide view off data density. Useful for showing shape of the data distribution

Question 6

Data density

Answer 6

Frequency of data in a given category. What you see in a histogram.

Question 7

Right or Left Skewed

Answer 7

When data trails of (diminishes) towards the right, it is right skewed.

When the data is less on the left, long thinner trail on the left, it is left skewed.

Question 8

Mode

Answer 8

Prominent peak in the distribution. Can be unimodal (1peak), bimodal (2peaks), or multimodal (greater than 2 peaks)

Question 9

Deviation

Answer 9

How far an observation/data point is from the mean

Question 10

Standard deviation

Answer 10

How far away the typical data point/observation is away from the mean. This varies from deviation in that it looks at the “typical” observation while deviation looks at the individual datum. Derived from the square root of the variance.

About 70% data within 1 standard deviation and 95% within 2 standard deviations, not a hard rule

Question 11

Variance

Answer 11

Average squared distance from the mean.

Sum of all deviation)^2/(#observations-1

Question 12

Box plot

Answer 12

Summarizes data set using 5 statistics (median, interquartile range, first quartile, third quartile, whiskers) while also plotting unusual observations, outliers.

Question 13

Median

Answer 13

Splits the data in half. Often confused with the mean. It takes the value from the observation that lands in the middle.

Question 14

Interquartile range

Answer 14

The middle 50% of the data/observations. IQR for short.

IQR=Q3-Q1

Question 15

First quartile

Answer 15

The first 25% of the observations

Question 16

Third Quartile

Answer 16

The 75th percentile our the last 25% of the observations

Question 17

Whiskers

Answer 17

The data inside the box within 1.5xIQR

Question 18

Outliers

Answer 18

Data bound the whiskers, 1.5xIQR

Purposes:

• ID strong skew in the distribution
• ID possible data collection or data entry errors
• insight into interesting properties of the data

Question 19

Transformations

Answer 19

Rescaling the data using functions to more readily make statistical models without destroying the statistical integrity

Question 20

Intensity Map

Answer 20

A map with colors to show variations of intensity

Question 21

Contingency Table

Answer 21

Summarizes data for two (categorical) variables, each value represents #of times that particular combination of variables outcomes occurred

Question 22

Stacked bar plot

Answer 22

Graphical display of contingency table.

Most useful when one variable =explanatory and one variable =response.

Question 23

Pie chart

Answer 23

A bar plot representing contingency table data useful for giving a high level overview

Question 24

Side-by-side box plot

Answer 24

Traditional tool for comparing across groups

Question 25

Null hypothesis (H-sub0)

Answer 25

Represents status quo; general statement or default position where there is no difference between two measured phenomena or that the two samples derive from the same general population Ex: H-sub0 : p >0.6

Question 26

Alternative Hypothesis (H-subA)

Answer 26

Position which states something is happening, a new theory is preferred instead of an old one. Ex: H-subA : p < 6