MGSC 291 Exam One List Fall 2020 PDF

Preview

Summary

Fren 110 study guide to utilize study practices...

Description

1 MGSC 291 Exam One List of Topics Chapter 1: - Terms: Population, Sample, Parameter, Statistic, Descriptive Statistics, Inferential Statistics - Data Types: Qualitative, Quantitative (Discrete Quantitative and Continuous Quantitative) - Data Measurement Scales: Nominal, Ordinal, Interval, and Ratio Chapter 2: - Graphs: Bar Charts and Pie Charts for Qualitative Data - R Code for boxplot and histogram Chapter 3: - Descriptive Statistics: Mean, Median, Mode, Quartiles, Percentiles, IQR, Range, Variance, Standard Deviation, Coefficient of Variation, Skewness, Outlier Detection, Empirical Rule, Mean-Variance Analysis - R Code for mean, median, variance, standard deviation, quartiles, and five number summary Chapter 4: - Rules of Probability. Compliment Rule, Addition Rule, Conditional Rule of Probability. - Mutually Exclusive and Independent Events Chapter 5: - Discrete Probability Distributions: find probabilities, cumulative probabilities, E(X), mean, variance, standard deviation. - Binomial probability distribution - Poisson Probability Distribution - R Code for Binomial probabilities and Poisson Probabilities Chapter 6: - Continuous probabilities (areas under the curve). Uniform and normal probabilities. - Using R to find uniform and continuous probabilities and inverse normal probabilities Chapter 7: - Sampling distribution for X and sampling distribution for p. Use R to find probabilities Chapter 8: - Confidence intervals. Confidence interval for the mean using a t distribution; confidence interval for the proportion. - Using R to calculate confidence intervals

2

Chapter 1: Background and Definitions  Descriptive versus inferential statistics Descriptive: collecting, organizing, presenting, and summarizing data Inferential: drawing conclusions about population 

Definition of sample and population Population: all the people, objects, or items of interest in a study Sample: a subset or portion of a population



Definition of statistic and parameter Parameter: a number used to describe a population Statistic: a number calculated from a sample and used to estimate a parameter.



Time series and cross-sectional data Time Series Data: variables measured at regular intervals over time (months, quarters, years) Cross-Sectional Data: when several variables are all measures at the same time point (or time frame)

Types of variables  Categorical (Qualitative) vs. Quantitative Qualitative versus Quantitative— Qualitative is inherently non-numeric (e.g., color of eyes, make of car, course grade). Quantitative is inherently numeric (time to walk to class, number of courses you are registered for) 

Discrete Quantitative vs. Continuous Quantitative Quantitative Discrete versus Quantitative Continuous. Discrete data are count data (number of courses you are registered for, number of students in class in person). Finite number of possible values and there are gaps between the values Quantitative data are measured data (time to walk to class, amount of coffee in your coffee cup). There are an infinite number of possible outcomes within a given interval

3

Measurement Scales  For qualitative either nominal or ordinal Nominal—used for qualitative data that can only be sorted into distinct categories (e.g., major is Acct, IB, Finance, Mktg, Mgmt, MGSC or state of legal residence is SC, NC, GA, etc., eye color) Ordinal—used for qualitative data than can be sorted in distinct categories that can then be ranked (e.g., year in college—freshman, sophomore, junior, senior or rental car categories—sub-compact, compact, midsize, luxury, etc.) 

For quantitative either interval or ratio Interval—used for quantitative data for which there is no “true zero” (for example, temperature in degrees Fahrenheit, SAT scores). It has properties of ordinal level data (can be sorted and ordered), but also the distance between successive orderings can be measured and stays the same across the scale. Manmade scale, not form meaningful ratios Ratio—used for quantitative data for which there is a true zero and therefore meaningful ratios can be formed (e.g., amount of coffee in your cup, amount of $$ in your checking account, amount of gas in your car, your height). Highest level of measurement, all properties of Interval Level Data but has a true zero… thus meaningful ratios can be formed

4

Chapter 2: Data Displays For Qualitative Variables 

Frequency and Relative Frequency Bar Charts



Pie Chart

5

For Quantitative Variables  Frequency and Relative Frequency Histograms show the Distribution of a Variable (overall pattern) and its shape, center, and variability. Frequency and relative frequency distributions are most useful when you have a large or at least fairly large data set (n > 150).  Cumulative frequency histograms. Shows the number of data values in the current interval and all the intervals below it. Useful when you want to know the number or percentage of observations below a certain value.

6 

Boxplots are also used to show the data distribution. They are most useful when the data set is small or medium size. They are based on the five number summary. They also indicate outliers in the data set. Good for side-to-side comparisons of variables. o Displays the 5-number summary (min, Q1, median, Q3, and max)

7 

Line graphs are used to show time series data. You plot your variable (such as Sales in Dollars versus the time period (Qtr1, Qtr2, Qtr3, Qtr4, Qtr5, Qtr6, etc.). o Display Quantitative Data Over Time o Time is displayed on the X-Axis o The value of the variable is on the Y-Axis o You can display several variables (measured in the same units) over the same time periods



Scatterplots – used to visualize the relationship between two quantitative variables

In R you should know how to construct a: barchart , boxplot, and histogram. See the notes for examples. Here is a barchart example:

Frequency oildata[1:5,] do?

Extra:

30

Question #1: Serum glucose levels were recorded for patients. Copy and paste the following line into your R concole. gluc...