MATH 231: Basic Statistics Homework #5 – Correlation and Regression: 1). Bi-lo Appliance Super-Store has outlets in several large metropolitan areas in New England. The general sales manager aired a commercial for a digital camera on selected local TV stations prior ro a sale starting on Saturday and ending on Sunday. She obtained the information for Saturday-Sunday digital camera sales at the various outlets and paired it with the number of times the advertisement was shown on local TV stations
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Due in class Feb 6 UCI ID_____________________________ MultipleChoice Questions (Choose the best answer‚ and briefly explain your reasoning.) 1. Assume we have a simple linear regression model: . Given a random sample from the population‚ which of the following statement is true? a. OLS estimators are biased when BMI do not vary much in the sample. b. OLS estimators are biased when the sample size is small (say 20 observations)
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median‚ and the standard deviation. c. Select the variable that refers to the seating capacity of the stadium. Find the mean‚ median‚ and the standard deviation. Solution The calculations for this problem appear in the attached Excel file “QNT-561 Week #6 Problem Set.xls”. The values found were: a. For the Team Salaries: Mean 73063563.27 Median 66191416.50 Standard Deviation 34233970.30 b. For the Stadium Age: Mean 28.20 Median 17.50 Standard Deviation 25.94 c. For
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Individual Assignment: Week 4 QNT 561 November 1‚ 2010 Lee Chang Question 5 In the following situations‚ decide whether you would use a personal interview‚ telephone survey‚ or self-administered questionnaire. Give your reasons. a) A survey of the residents of a new subdivision on why they happened to select that area in which to live. You also wish to secure some information about what they like and do not like about life in the subdivision. In this situation I would use a personal
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Week Four Team Paper xxxxxxxxxxxxxx QNT/561 August 1‚ 2012 xxxxxxxxx Week 4 Team Paper Best Buy is a company that has 40 years of history with a very accomplished sense of success. In 1966 Best Buy was a small electronics store in that originated in St. Paul Minnesota by Richard Schulze and an acquainted business partner. Considering that technology changes so rapidly‚ Best Buy has had to transform from just being the little electronics store down the way into a competitive
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Simple Linear Regression Model 1. The following data represent the number of flash drives sold per day at a local computer shop and their prices. | Price (x) | Units Sold (y) | | $34 | 3 | | 36 | 4 | | 32 | 6 | | 35 | 5 | | 30 | 9 | | 38 | 2 | | 40 | 1 | | a. Develop as scatter diagram for these data. b. What does the scatter diagram indicate about the relationship between the two variables? c. Develop the estimated regression equation and explain what the
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considers the relationship between two variables in two ways: (1) by using regression analysis and (2) by computing the correlation coefficient. By using the regression model‚ we can evaluate the magnitude of change in one variable due to a certain change in another variable. For example‚ an economist can estimate the amount of change in food expenditure due to a certain change in the income of a household by using the regression model. A sociologist may want to estimate the increase in the crime rate
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OF TECHNOLOGY AND INNOVATION Degree Level 1 Quantitative Skills Correlation & Regression Intake : Lecturer : Date Assigned : Date Due : 1. Suppose that a random sample of five families had the following annual income and savings. Income (X) Savings (Y) (£’000) (£’000) 8 0.6 11 1.3 9 1.0 6 0.7 5 0.3 (a) Obtain the least square regression equation of savings (Y) on income (X) and plot the regression line on a graph. (b) Estimate the savings if the family income
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Correlation and Regression Assignment Problem 1. a. Explain which variable you chose as the explanatory variable and discuss why. * The explanatory variable is the height. This is because I am assuming that as height increases‚ the weight will increase as well. So the weight is the dependent variable b. Produce a scatter plot and insert the result here. * Scatter plot c. Find the equation of the regression line‚ Write it in the form of y=a+bx‚ where a is the y-intercept
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Descriptive Statistics QNT/561 July 29‚ 2014 Descriptive Statistics Job Satisfaction Central Tendency: Mean=8.5 JDI Dispersion: Standard Deviation=1.16 JDI Number: 139 Min/Max: 7 to 10 JDI Confidence Interval: 8.36 to 8.75 JDI *JDI=Job Descriptive Index Months of Employment Central Tendency: Mean= 136.24 Months Dispersion: Standard Deviation= 117.26 Months Number: 139 Min/Max: 1 to 359 Months Confidence Interval: 116.74 to 155.73 Months Descriptive
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