Statistical Analysis
The analysis of the data from the study of Barnes & Noble stores is in two stages, the descriptive study and inferential statistical study. Initially, the Team will distribute and collect the questionnaires. The use of classification will summarize the data and express it in the tabular form for better understanding of the data. For example, if the questionnaires consist of information from males and females, the data is putinto two categories and expressed in a table form. A proper graphical method can display the data summation. As an example, the display of information can be in a pie chart or simple bar chart of different categories like people who prefer to read electronic books and people who prefer to read hard copy books. Finally, a proper statistical hypothesis testing method can answer the research questions in the questionnaire. In the Barnes & Noble case study, the hypothesis test that proportion of people who prefer to read the electronic copy of books is larger than the proportion of people who prefer to read hard copy books. The hypothesis test consists of equality of proportions of two populations. The hypothesis test can assist in determining if the emergence of electronic copy of books is reducing the prices of hard bound and paperback books.

Analyzing the Data
The selection of the analysis is based on two things: the way the hypothesis is stated in statistical language and the level of measurement of the variable. The Hypothesis
The way the researcher states the hypothesis makes a difference in the data analysis. Here are three null hypothesis examples: (1) Variable A does not relate to Variable B, (2) Variable A does not predict to Variable B, (3) There are no differences on Variable A by Variable B. (1) tends to be stated in correlation or chi-square language, (2) in regression language, and (3) in ANOVA or perhaps Mann-Whitney language. How is one to choose the precise data analysis? It...

...Elementary Concepts in Statistics. In this introduction, we will
briefly discuss those elementary statistical concepts that provide the necessary
foundations for more specialized expertise in any area of statistical data analysis. The
selected topics illustrate the basic assumptions of most statistical methods and/or have
been demonstrated in research to be necessary components of one's general
understanding of the "quantitative nature" of reality (Nisbett, et al., 1987). Because of
space limitations, we will focus mostly on the functional aspects of the concepts
discussed and the presentation will be very short. Further information on each of those
concepts can be found in the Introductory Overview and Examples sections of this
manual and in statistical textbooks. Recommended introductory textbooks are:
Kachigan (1986), and Runyon and Haber (1976); for a more advanced discussion of
elementary theory and assumptions of statistics, see the classic books by Hays (1988),
and Kendall and Stuart (1979).
• What are variables?
• Correlational vs.
experimental research
• Dependent vs. independent
variables
• Measurement scales
• Relations between variables
• Why relations between
variables are important
• Two basic features of every
relation between variables
• What is "statistical
significance" (p-value)
• How to determine that a
result is "really" significant
•...

...
UNIVERSITY OF LA VERNE
COLLEGE OF BUSINESS AND PUBLIC MANAGEMENT
BUS 500C
QUANTITATIVE & STATISTICALANALYSIS
COMPREHENSIVE FINAL EXAMINATION
1. The personnel director for a business organization has identified 10 individuals as qualified candidates for 3 managerial training positions her firms seeks to fill. Use the appropriate rule to give the number of different combinations of the 10 individuals who could be chosen for the 3 positions.
As discussed in class we would use the combination approach. The primary reason is that they are all identical (qualified) positions and thus the order would not matter.
Data: The variable is the total number of elements = n (3) and r (10) designates the number of groups and is expressed by the following formula from our textbook (Keller, 2012).
The formula that produces the result of 120 combinations is.
= 10! = 10x9x8x7x6x5x4x3x2x1 3628800 = 120
3(10-3)! (3x2x1)(7x6x5x4x3x2x1) = 6(5040)
2. The president, vice president, secretary, and treasurer are to be selected from a group of 10 candidates. Use the appropriate rule to give the number of ways the positions may be filled.
Based on class discussion and my studies, and the facts provided, the permutation rule would be used to determine the answer. Applying this formula there would be 5040 permutations.
Order would matter, because there is a order or value to the four positions and each of the candidates could be combined...

...Distributions 153
6 Continuous Probability Distributions
187
7 Sampling and Sampling Distributions 219
8 Interval Estimation
9 Hypothesis Tests
251
283
10 Statistical Inference about Means and Proportions with Two
Populations
335
I I Inferences about Population Variances
373
12 Tests of Goodness of Fit and Independence 399
13 Analysis of Variance and Experimental Design 429
14 Simple linear Regression 489
15 Multiple Regression 555
16 Regression Analysis: Model Building 613
17 Index Numbers 659
18 Forecasting 683
ix
,
BRIEF CONTENTS
19 Non-parametric Methods
727
20 Statistical Methods for Quality Control
21 Decision Analysis
799
22 Sample Surveys (on CD)
Appendix A References and Bibliography 835
Appendix B Tables 837
Appendix C Summation Notation 867
Appendix D Answers to Even-numbered Exercises 870
Glossary 9 I I
Index 920
767
Contents
Preface and Acknowledgments xvii
About the Authors xx
Walk-through Tour xxii
Accompanying Website xxiv
Supplements xxv
J Descriptive Statistics:
Measures 67
Statistics in practice: TV audience measureme~1 69
3.2
1.2
Data sources 8
1.4
Descriptive statistics I2
1.5
Statistical inference 14
1.6
3.3
Data 5
1.3
Computers and statisticalanalysis 15
Exercises 1-13 I 6
Summary 20
Key terms 20
~~...

...Regression Analysis (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 regression analysis 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 Regression Analysis 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 variables, it is concluded that there is a relationship between...

...Analysis and Findings This chapter presents a description of the sample and analysis results based on the questionnaire distributed to liable officers from the civil service of Mauritius. It provides an investigation of the impact of a strategic dimension to the management of the human resource on organizational effectiveness in the civil service. The data was organized and evaluated with the SPSS (Statistical Package of Social Sciences) software version 16.0 and for the evaluation; descriptive statistics (frequency distribution, percentages and means), correlations, chi square test, independent sample t-test, Anova test and regression analysis were employed. Population and Response rate A low response rate can raise questions according to whether the response received were representative of the sample or were in some way biased (Punch, 2003). However the researcher should endeavour for a response rate of at least 60 percent. Therefore, as regards to this study, 140 questionnaires were distributed to the customers and 105 questionnaires were collected two weeks later. All the collected questionnaires were deemed good to be analyzed. Hence, a feedback of 75% in this research is therefore taken to be acceptable. Reliability of data Reliability in general means stability of response. This concerns whether the same respondents would answer the same questions in the same way if they were asked again. Reliability is the...

...negatively correlated? Use the 0.5 significance level. Report the P-value of the test.
Answer to the Question No 7
I. Let selling price be the dependent variable and size of the home the independent variable.
Then run the regression analysis in SPSS and we get,
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.471(a)
.222
.201
398.7963
a Predictors: (Constant), Size of the Home in Square Feet
Coefficients(a)
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
95% Confidence Interval for B
B
Std. Error
Beta
Lower Bound
Upper Bound
1
(Constant)
323.827
575.402
.563
.577
-360.456
1590.511
Size of the Home in Square Feet
.841
.259
.471
3.252
.002
.235
1.258
a Dependent Variable: Selling Price in TK. 000 (Thousand)
The regression equation, =323.827+.841 X
Where, X = size of homes
= Selling price of homes
The estimated Selling price of homes with an area of 2200 square feet,
=323.827+.841 *(2200) = $2174.027.
95% confidence interval for the selling price of a home is (.235-1.258).
II. Let selling price be the dependent variable and distance from the center of the city the independent variable,Then run the regression analysis in SPSS and we get,
Coefficients(a)
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
95% Confidence Interval for B
B
Std. Error
Beta
Lower...

...Statistical Data Analyses
Graeme Ferdinand D. Armecin, MHSS
Outline of Presentation
Overview of Research Designs Functions of Statistics Sampling Principles of Analysis and Interpretation (with Computer Package) – Descriptive Statistics – Inferential Statistics
Graeme Ferdinand D. Armecin, MHSS
Statistical Data Analyses
Purposes of Research Design
Exploratory/Descriptive Research design – Basic or fundamental in the research enterprise – What is going on? – Example: What is the percentage of the population enrolled in health insurance programs? Explanatory Research design – Why is it going on? – The purpose is to avoid invalid inferences – Example: What is the effect of enrolment in health insurance programs to the cost of hospitalization?
Graeme Ferdinand D. Armecin, MHSS
Statistical Data Analyses
Essential Elements of Research
Theory (literature)
Statistics
Research empirical/what is observed Design
Statistical Data Analyses
Graeme Ferdinand D. Armecin, MHSS
Functions of Statistics
1.
2.
3.
Characterizing/exploring patterns of data to make it more meaningful Making generalizations about population parameter from sample statistic Finding associations or relationships between and among the gathered data
Graeme Ferdinand D. Armecin, MHSS
Statistical Data Analyses
Sampling
Sampling refers to taking a portion of...

...
MATH533: Applied Managerial Statistics
PROJECT PART C: Regression and Correlation Analysis
Using MINITAB perform the regression and correlation analysis for the data on SALES (Y) and CALLS (X), by answering the following questions:
1. Generate a scatterplot for SALES vs. CALLS, including the graph of the "best fit" line.
Interpret.
After interpreting the scatter plot, it is evident that the slope of the ‘best fit’ line is positive, which indicates that sales amount varies directly with calls. As call increases, the sales amount increases as well.
2. Determine the equation of the "best fit" line, which describes the relationship between
SALES and CALLS.
The equation of the ‘best fit’ line or the regression equation is SALES(Y) = 9.638 + 0.2018 CALLS(X1)
3. Determine the coefficient of correlation. Interpret:
MINTAB Results:
Correlations: SALES(Y), CALLS(X1)
Pearson correlation of SALES(Y) and CALLS(X1) = 0.871
P-Value = 0.000
The coefficient of correlation is 0.871. The correlation coefficient is positive so this indicates a positive or direct relationship between the variables. The correlation coefficient is far from the P-Value of 0.000. This means that there is an extremely low chance that Sales and Calls results are wrong and we can be confident in interpretation.
4. Determine the coefficient of determination. Interpret.
MINTAB Results:
S = 2.05708 R-Sq = 75.9% R-Sq(adj) = 75.7%
The...