Solution Manual For Business Statistics 3rd Edition by Robert Donnelly, ISBN: 9780134685267, All Chapters 1 to 18 Covered, Verified Latest Edition
Solution Manual For Business Statistics 3rd Edition by Robert Donnelly All 1-18 Chapters Covered ,Latest Edition, ISBN:9780134685267
Solution Manual For Business Statistics 3rd Edition by Robert Donnelly, ISBN: 9780134685267, All 18 Chapters Covered, Verified Latest Edition
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INSTRUCTOR’S SOLUTIONS
MANUAL
DIRK TEMPELAAR
Maastricht University
BUSINESS STATISTICS
THIRD EDITION
Robert A. Donnelly
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Table of Contents
Chapter 1: An Introduction to Business Statistics………………………………………………1-1
Chapter 2: Displaying Descriptive Statistics……………………………………………………2-1
Chapter 3: Calculating Descriptive Statistics………………………………………...…………3-1
Chapter 4: Introduction to Probabilities…………………………………………...……………4-1
Chapter 5: Discrete Probability Distributions…………………………………………..………5-1
Chapter 6: Continuous Probability Distributions……………………………………………….6-1
Chapter 7: Sampling and Sampling Distributions………………………………………………7-1
Chapter 8: Confidence Intervals………………………………………………………………...8-1
Chapter 9: Hypothesis Testing for a Single Population………………………………………...9-1
Chapter 10: Hypothesis Tests Comparing Two Populations…………………………………..10-1
Chapter 11: Analysis of Variance (ANOVA) Procedures……………………………………..11-1
Chapter 12: Chi-Square Tests………………………………………………………………….12-1
Chapter 13: Hypothesis Tests for the Population Variance……………………………………13-1
Chapter 14: Correlation and Simple Linear Regression……………………………………….14-1
Chapter 15: Multiple Regression and Model Building………………………………………...15-1
Chapter 16: Forecasting………………………………………………………………………..16-1
Chapter 17: Decision Analysis………………………………………………………………...17-1
Chapter 18: Nonparametric Statistics………………………………………………………….18-1
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CHAPTER 1 An Introduction to Business Statistics
1.1 Quantitative/Interval. The differences between average monthly temperatures are
meaningful, but there is no true zero point, i.e., absence of temperature.
1.2 Quantitative/Ratio. The differences between average monthly rainfalls are meaningful, and
there is a true zero point, because there may be a month without any rainfalls.
1.3 Qualitative/Ordinal. You can rank education level, but the differences between different
educational levels cannot be measured.
1.4 Qualitative/Nominal. The marital status is just a label without a meaningful difference, or
ranking.
1.5 Quantitative/Ratio. The differences between ages of respondents are meaningful and there is
a true zero point: an age of the respondents that equals zero represents the absence of age.
1.6 Qualitative/Nominal. The genders are merely labels with no ranking or meaningful
difference.
1.7 Quantitative/Interval. The differences between birth years are meaningful, but there is no true
zero point with calendar years.
1.8 Qualitative/ Nominal. The political affiliations are merely labels with no ranking or
meaningful difference.
1.9 Qualitative/ Nominal. The races of the respondents are merely labels with no ranking or
meaningful difference.
1.10 Qualitative/ Ordinal. You can rank the performance rating, but the differences between
different performance ratings cannot be measured.
1.11 Qualitative/ Nominal. The uniform numbers of each member of the school’s sport team are
labels with no ranking or meaningful difference.
1.12 Qualitative/ordinal. The differences in the data values between class ranks are not
meaningful.
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1.13 Quantitative/Ratio. The differences between final exam scores for your statistics class are
meaningful, and there is a true zero point because a student who did not take the exam would
have a score of zero.
1.14 Qualitative/Nominal. The state in which the respondents in a survey reside is a label and it
is meaningless to talk about the rating of this value.
1.15 Quantitative/Interval. The differences between SAT scores for graduating high school
students are meaningful, but there is no true zero point because a student with an SAT score
equal to zero does not indicate the absence of a score.
1.16 Qualitative/Ordinal. You can rank movie ratings, but the differences between different
ratings cannot be measured.
1.17 Qualitative/ordinal. The differences in the data values between ratings are not meaningful.
1.18 Qualitative/ordinal. The differences in the data values between ratings are not meaningful.
1.19 Cross-sectional
1.20 Time series
1.21 Time series: Men weekly earnings over the five years. Time series: Women weekly earnings
over the five years.
1.22 Cross-sectional data: Men and women workers weekly earnings for any one particular year.
1.23 Cross-sectional: The number of 8x10, 11x14 and 13x19 prints sold over a particular year.
1.24 Time series: the number of 8x10 prints sold over the four years. Time series: the number of
11x14 prints sold over the four years. Time series: the number of 13x19 prints sold over the four
years.
1.25 Descriptive statistics, because it identifies a sample mean.
1.26 Inferential statistics, because the statements about comparing the average costs of a hotel
room in two states was based on results from samples taken from two populations.
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