This course prepares students to apply statistics and probability concepts to business decisions. Students learn important criterion for developing effective research questions, including the creation of appropriate sampling populations and instruments. Other topics include descriptive statistics, probability concepts, confidence intervals, sampling designs, data collection, and data analysis—including parametric and nonparametric tests of hypothesis and regression analysis.

Policies

Faculty and students will be held responsible for understanding and adhering to all policies contained within the following two documents:

• University policies: You must be logged into the student website to view this document. • Instructor policies: This document is posted in the Course Materials forum.

University policies are subject to change. Be sure to read the policies at the beginning of each class. Policies may be slightly different depending on the modality in which you attend class. If you have recently changed modalities, read the policies governing your current class modality.

Course Materials

Cooper, D. R., & Schindler, P.S. (2011). Business research methods (11th ed.). New York, NY: McGraw-Hill/Irwin.

McClave, J. T., Benson, P. G., & Sincich, T. (2011). Statistics for business and economics (11th ed.). Boston, MA: Prentice Hall.

All electronic materials are available on the student website.

|Week One: Descriptive Statistics and Probability Distributions | | |Details |Due |Points | |Objectives |Compute descriptive statistics for given data sets. | | | | |Apply probability concepts related to discrete and continuous probability. | | | |Reading |Read Ch. 2 of Statistics for Business and Economics. | | | |Reading |Read Ch. 4 of Statistics for Business and Economics. | | | |Participation |Participate in class discussion. | |3 | |Nongraded Activities and|Create the Learning Team Charter. | | | |Preparation | | | | |Learning Team Charter | | | | |Nongraded Activities and|Log on to MyStatLab® on the student website. | | | |Preparation | | |...

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Unit 5 – RegressionAnalysis
Mikeja R. Cherry
American InterContinental University
Abstract
In this brief, I will demonstrate selected perceptions of the company Nordstrom, Inc., a retailer that specializes in fashion apparel with over 12 million dollars in sales last year. I will research, review, and analyze perceptions of the company, create graphs to show qualitative and quantitative analysis, and provide a summary of my findings.
Introduction
Nordstrom, Inc. is a retailer that specializes in fashion apparel for men, women and kids that was founded in 1901. The company is headquartered in Seattle, Washington with over 61,000 employees world-wide as of February 2, 2013. (Business Wire, 2014)
Nordstrom, Inc. offers on online store, e-commerce, retail stores, mobile commerce and catalogs to its consumers. It operates 117 full-line stores within the United States and 1 store in Canada, 167 Nordstrom Rack stores, 1 clearance store under the Last Chance Banner, 1 philanthropic treasure & bond store called Trunk Club and 2 Jeffrey boutiques. The option of shopping online is also available at www.nordstrom.com along with an online private sale subsidiary Hautelook. They have warehouses, also called fulfillment centers, which manages majority of their shipping needs that are located in Cedar Rapids, Iowa. (Business Source Premier, 2014)
Nordstrom, Inc. continues to make investments in their...

...Quantitative Methods Project
RegressionAnalysis for the pricing of players in the
Indian Premier League
Executive Summary
The selling price of players at IPL auction is affected by more than one factor. Most of these factors affect each other and still others impact the selling price only indirectly. The challenge of performing a multiple regressionanalysis on more than 25 independent variables where a clear relationship cannot be obtained is to form the regression model as carefully as possible.
Of the various factors available we have leveraged SPSS software for running our regressionanalysis. One of the reasons for preferring SPSS over others was the ease with which we can eliminate extraneous independent variables. The two methodologies used for choosing the best model in this project are:
* Forward Model Building:
Independent variables in order of their significance are incrementally added to the model till we achieve the optimum model.
* Backward Elimination:
The complete set of independent variables is regressed and the least significant predictors are eliminated in order to arrive at the optimum model.
Our analysis has shown that the following variables are the most significant predictors of the selling price:...

...RegressionAnalysis Exercises
1- A farmer wanted to find the relationship between the amount of fertilizer used and the yield of corn. He selected seven acres of his land on which he used different amounts of fertilizer to grow corn. The following table gives the amount (in pounds) of fertilizer used and the yield (in bushels) of corn for each of the seven acres.
|Fertilizer Used |Yield of Corn |
|120 |138 |
|80 |112 |
|100 |129 |
|70 |96 |
|88 |119 |
|75 |104 |
|110 |134 |
a. With the amount of fertilizer used as an independent variable and yield of corn as a...

...number of
months that has the negative returns is 22. In order to test the hypothesis, he should do chi-square test. First of all he calculates the expected value that has the positive and negative returns respectively, that is 54 (n) × 0.5 = 27 in each case as below (The expected value is more than 5, so he can use chi-square test). AT&T (Actual) Positive 32 Negative 22 AT&T (Expected) Positive 27 Negative 27
And he calculates the chi-square value, χ2 = [(32 – 27)2/27] + [(22 – 27)2/27] = 25/27 + 25/27 = 1.8519
Cohort 2- Team 5
Page 1
Because χ2 is less than χ20.1 = 2.70544 (degree of freedom = 2 (np) – 1 = 1, np is number of probability, ppositive and pnegative), so he doesn’t reject H0 at 10 % significance level. The p value of Excel calculation is 0.1736 (p value >α = 0.1, so do not reject H0). That means that the number of
months of positive return and of negative return is the same.
But he is still suspicious of this result because the actual value of the positive return is bigger than that of the negative return. It looks like nearly twice as many positive returns as negative returns. Therefore he sets an additional hypothesis; the null hypothesis H0 : ppositive = 2 × pnegative versus the alternative hypothesis Ha : ppositive ≠ 2 × pnegative. Then he acquires the expected positive return, 54 (n) × 2 / 3 = 36 and the expected negative return, 54 (n) × 1 / 3 = 18 (The expected value is more than 5, so he can use chi-square...

...l
RegressionAnalysis
Basic Concepts & Methodology
1. Introduction
Regressionanalysis is by far the most popular technique in business and economics for
seeking to explain variations in some quantity in terms of variations in other quantities, or to
develop forecasts of the future based on data from the past. For example, suppose we are
interested in the monthly sales of retail outlets across the UK. An initial dataanalysis would
summarise the variability in terms of a mean and standard deviation, but the variation from
outlet to outlet could be very large for a variety of reasons. The size of the local market, the
size of the shop, the level of competition, the level of advertising, etc.. would all influence the
sales volume from outlet to outlet. This is where regressionanalysis can be useful. A
regressionanalysis would seek to model the influence of these factors on the level of sales. In
statistical terms we would be seeking to regress the variation in sales ⎯ the dependent
variable ⎯ upon several explanatory variables such as advertising, size, etc..
From a forecasting point of view we can use regressionanalysis to develop predictions. If we
were asked to make a forecast for the monthly sales of a proposed new outlet in, say, Oxford,
we can simply compute the average outlet sales and put this...

...associated with a β1 change in Y.
(iii) The interpretation of the slope coefficient in the model ln(Yi ) = β0 + β1 ln(Xi ) + ui is as
follows:
(a) a 1% change in X is associated with a β1 % change in Y.
(b) a change in X by one unit is associated with a β1 change in Y.
(c) a change in X by one unit is associated with a 100β1 % change in Y.
(d) a 1% change in X is associated with a change in Y of 0.01β1 .
(iv) To decide whether Yi = β0 + β1 X + ui or ln(Yi ) = β0 + β1 X + ui fits the data better, you
cannot consult the regression R2 because
(a) ln(Y) may be negative for 0 < Y < 1.
(b) the TSS are not measured in the same units between the two models.
(c) the slope no longer indicates the effect of a unit change of X on Y in the log-linear
model.
(d) the regression R2 can be greater than one in the second model.
1
(v) The exponential function
(a) is the inverse of the natural logarithm function.
(b) does not play an important role in modeling nonlinear regression functions in econometrics.
(c) can be written as exp(ex ).
(d) is ex , where e is 3.1415...
(vi) The following are properties of the logarithm function with the exception of
(a) ln(1/x) = −ln(x).
(b) ln(a + x) = ln(a) + ln(x).
(c) ln(ax) = ln(a) + ln(x).
(d) ln(xa) = aln(x).
(vii) In the log-log model, the slope coefficient indicates
(a) the effect that a unit change in X has on Y.
(b) the elasticity of Y with respect to X.
(c) ∆Y/∆X.
(d)
∆Y
∆X
×
Y
X
(viii) In the...

...REGRESSIONANALYSIS
Correlation only indicates the degree and direction of relationship between two variables. It does not, necessarily connote a cause-effect relationship. Even when there are grounds to believe the causal relationship exits, correlation does not tell us which variable is the cause and which, the effect. For example, the demand for a commodity and its price will generally be found to be correlated, but the question whether demand depends on price or vice-versa; will not be answered by correlation.
The dictionary meaning of the ‘regression’ is the act of the returning or going back. The term ‘regression’ was first used by Francis Galton in 1877 while studying the relationship between the heights of fathers and sons.
“Regression is the measure of the average relationship between two or more variables in terms of the original units of data.”
The line of regression is the line, which gives the best estimate to the values of one variable for any specific values of other variables.
For two variables on regressionanalysis, there are two regression lines. One line as the regression of x on y and other is for regression of y on x.
These two regression line show the average relationship between the two variables. The regression line of y on x gives the most probable...

...RegressionAnalysis (Tom’s Used Mustangs)
Irving Campus
GM 533: Applied Managerial Statistics
04/19/2012
Memo
To:
From:
Date: April 19st, 2012
Re: Statistic Analysis on price settings
Various hypothesis tests were compared as well as several multiple regressions in order to identify the factors that would manipulate the selling price of Ford Mustangs. The data being used contains observations on 35 used Mustangs and 10 different characteristics.
The test hypothesis that price is dependent on whether the car is convertible is superior to the other hypothesis tests conducted. The analysis performed showed that the test hypothesis with the smallest P-value was favorable, convertible cars had the smallest P-value.
The data that is used in this regressionanalysis to find the proper equation model for the relationship between price, age and mileage is from the Bryant/Smith Case 7 Tom’s Used Mustangs. As described in the case, the used car sales are determined largely by Tom’s gut feeling to determine his asking prices.
The most effective hypothesis test that exhibits a relationship with the mean price is if the car is convertible. The RegressionAnalysis is conducted to see if there is any relationship between the price and mileage, color, owner and age and GT. After running several models with different independent...