MGSC 291 EXAM 1 (WALTERS) STUDY GUIDE
Descriptive Statistics - Answers -Collecting, summarizing, and presenting data
Inferential Statistics - Answers -drawing conclusions about a population based on
sample data from that population
Parameter - Answers -a number used to describe a population
Statistic - Answers -a number calculated from a sample and is used to estimate the
parameter
Time Series - Answers -variables that are measured at regular intervals over time
(hourly, daily, weekly, monthly, quarterly, annual...)
Cross-Sectional Data - Answers -several variables are all measured at the same time
point (or time frame)
Big Data - Answers -Data warehouses that have grown enormously in size, with the
use of powerful computers, the information contained in them is accessible and used to
help make decisions
Discrete variables - Answers -jumps between possible values
continuous variable` - Answers -another possible value between any two values
Nominal variables - Answers -categorical variables for which the categories do not
have a natural ordering
Ordinal Variables - Answers -categorical variables for which the categories have a
natural ordering
boxplots - Answers -For quantitative data
works for small to large datasets
plots the five number summary
great for side-by-side comparisons
R code for boxplots - Answers -boxplot()
Histogram - Answers -medium to large quantitative datasets
Bins touch
Choice of number of bins can distort features of the shape of the distribution
R code for Histograms - Answers -hist()
, Line graph - Answers -Displays quantitative data changing over time
Time on x axis (horizontal)
Variable on y axis (vertical)
Bar Graph - Answers -Use for qualitative data
Can be horizontal or vertical
can display parts of a whole or separate values
For nominal data: put in ascending/descending order
For ordinal data: put in order of categories
Pie Charts - Answers -Categorical Data
Not Good for comparisons
Do not use
Scatterplot - Answers -Used to depict two potentially related variables
Each point is a pairing
Linear, curvilinear, or no relationship
Positive vs negative relationship
Sample Mean - Answers -Average of sample
Median - Answers -50th percentile of the sample
Middle observation in the ordered list if "n" is odd
Average of 2 middle observations if "n" is even
If data is skewed to the right: - Answers -mean > median
If data is skewed to the left: - Answers -mean<median
Coefficient of Variation - Answers -Standard deviation expressed as a percent of the
mean (( sd/mean) x 100)
Chebyshev's Inequality - Answers -1-1/k^2
Empirical Rule - Answers -The rules gives the approximate % of observations w/in 1
standard deviation (68%), 2 standard deviations (95%) and 3 standard deviations
(99.7%) of the mean when the histogram is well approx. by a normal curve
Z-Score - Answers -(x-mean)/SD
Kurtosis - Answers -"tailedness" of the distribution
Complement Rule - Answers -P(A^c) = 1 - P(A)
Union Rule - Answers -P(A U B) = P(A) + P(B) - P(A ∩ B)
Descriptive Statistics - Answers -Collecting, summarizing, and presenting data
Inferential Statistics - Answers -drawing conclusions about a population based on
sample data from that population
Parameter - Answers -a number used to describe a population
Statistic - Answers -a number calculated from a sample and is used to estimate the
parameter
Time Series - Answers -variables that are measured at regular intervals over time
(hourly, daily, weekly, monthly, quarterly, annual...)
Cross-Sectional Data - Answers -several variables are all measured at the same time
point (or time frame)
Big Data - Answers -Data warehouses that have grown enormously in size, with the
use of powerful computers, the information contained in them is accessible and used to
help make decisions
Discrete variables - Answers -jumps between possible values
continuous variable` - Answers -another possible value between any two values
Nominal variables - Answers -categorical variables for which the categories do not
have a natural ordering
Ordinal Variables - Answers -categorical variables for which the categories have a
natural ordering
boxplots - Answers -For quantitative data
works for small to large datasets
plots the five number summary
great for side-by-side comparisons
R code for boxplots - Answers -boxplot()
Histogram - Answers -medium to large quantitative datasets
Bins touch
Choice of number of bins can distort features of the shape of the distribution
R code for Histograms - Answers -hist()
, Line graph - Answers -Displays quantitative data changing over time
Time on x axis (horizontal)
Variable on y axis (vertical)
Bar Graph - Answers -Use for qualitative data
Can be horizontal or vertical
can display parts of a whole or separate values
For nominal data: put in ascending/descending order
For ordinal data: put in order of categories
Pie Charts - Answers -Categorical Data
Not Good for comparisons
Do not use
Scatterplot - Answers -Used to depict two potentially related variables
Each point is a pairing
Linear, curvilinear, or no relationship
Positive vs negative relationship
Sample Mean - Answers -Average of sample
Median - Answers -50th percentile of the sample
Middle observation in the ordered list if "n" is odd
Average of 2 middle observations if "n" is even
If data is skewed to the right: - Answers -mean > median
If data is skewed to the left: - Answers -mean<median
Coefficient of Variation - Answers -Standard deviation expressed as a percent of the
mean (( sd/mean) x 100)
Chebyshev's Inequality - Answers -1-1/k^2
Empirical Rule - Answers -The rules gives the approximate % of observations w/in 1
standard deviation (68%), 2 standard deviations (95%) and 3 standard deviations
(99.7%) of the mean when the histogram is well approx. by a normal curve
Z-Score - Answers -(x-mean)/SD
Kurtosis - Answers -"tailedness" of the distribution
Complement Rule - Answers -P(A^c) = 1 - P(A)
Union Rule - Answers -P(A U B) = P(A) + P(B) - P(A ∩ B)
