TY! Problem # 3. A random sample of 8 office staffs hired within the last year was selected from a largecorporation. For each selected office staff, his or her experience (in months) at the time of hire andstarting salary were recorded. The data is given in the table below.Experience (in months)1368102079 15Starting Salary (in P000s)2014 16192112 13 21Determine the regression equation. Solve for the sum of squares for error, standard error, coefficient ofdetermination. Find the 99% confidence interval for all months for the 10th month. Determine a 99%prediction interval for the 10th month.Solution:Complete the table.XYX²XY(Y-8)²(Y-8)²1320614816101920217129131521Solve for the following:ΣΧ=ΣΧ =ΣΥΞZY²=(Y-9)²=Slope (b₁)=,Simple Linear Regression Equation (Y=b₂x + b₂)Standard Error of Estimate (SE):Coefficient Determination (r):Confidence Interval:X =ΣΧΥ =Σ(Y - 9)² =Intercept (bo) =Prediction Interval:Y =

The question is based on regression analysis. Given data :

Answered: Problem # 3. A random sample of 8…

College Algebra

7th Edition

ISBN:9781305115545

Author:James Stewart, Lothar Redlin, Saleem Watson

Publisher:James Stewart, Lothar Redlin, Saleem Watson

Chapter1: Equations And Graphs

Section: Chapter Questions

Problem 10T: Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s...

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Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.
Problem 2 The following printout shows the results of a simple linear regression model that predicts monthly sales (in thousands of dollars) based on how much money was spent on advertising (in thousands of dollars) during a particular month for 15 stores of a retail chain. a) Is there a statistically significant relationship between money spent on advertising and sales? Test at the 5% level of significance and explain your approach (including hypotheses). b) Somebody claims that every additional $1,000 in advertising will increase sales by more than $2,000 in the population. Can you find support for this claim given the results of your analysis? Test at the 5% level of significance and explain your approach (including hypotheses). How is this test different from the one in part a)? c) Find a 95% confidence interval for the change in sales given a $1,000 increase in the amount of money spent on advertising. How does this confidence interval relate to your answer to part a)?
QUESTION 2 XXX Electric Illuminating Company is doing a survey on the relationship between electricity used in kilowatt-hours (thousand) and the number of rooms in a private single-family residence. A random sample of 10 homes was selected and the electricity consumption recorded as below. ii. Find a suitable linear regression equation ? = ? + ??. iii. Determine the number of kilowatt-hours (thousand) for an eleven-room residence.
The operations manager of a musical instrument distributor feels that the demand for Bass Drums may be related to the number of television appearances by the popular rick group Green Shades during the previous month. The manager has collected the data shown in the following table. Demand for Bass Drums 3 6 7 5 10 8 Green Shades TV appearances 3 4 7 6 8 5 Develop the linear regression equation to forecast. Forecast demand for Bass Drums when Green Shades’ TV appearances are 10. Compute MSE and standard deviation for Problem 8.
Question 16 Regression analysis was applied between sales (in $1000s) and advertising (in $100s), and the following regression function was obtained. = 500 + 4x Based on the above estimated regression line, if advertising is $10,000, then the point estimate for sales (in dollars) is _____. $505,000 $900 $40,500 $900,000
Do students with higher college grade point averages (GPAs) earn more than those graduates with lower GPAs?† Consider the following hypothetical college GPA and salary data (10 years after graduation). GPA Salary ($) 2.22 72,000 2.29 48,000 2.57 72,000 2.59 64,000 2.77 88,000 2.85 98,000 3.12 133,000 3.35 130,000 3.66 157,000 3.68 162,000 Use these data to develop an estimated regression equation that can be used to predict annual salary 10 years after graduation given college GPA. (Let x = GPA, and let y = salary (in $). Round your numerical values to the nearest integer.) ŷ = Find the value of the test statistic. (Round your answer to two decimal places.) = Find the p-value. (Round your answer to three decimal places.) p-value =
Problem 2: You have to examine the relationship between the age and price for used cars sold in the last year by a car dealership company to build a predictive linear regression model. The age of the car is the independent variable for your model. Here is the table of the data: Car Age in Years Price in Dollars 4 6,300 4 5,800 5 5,700 5 4,500 7 4,500 7 4,200 8 4,100 9 3,100 10 2,100 11 2,500 12 2,200 What would we expect for the price of an 8.5 year-old car?
given the information below: Row Education Income in in Years 2015 Dollars 1 7 22587 2 10 28305 3 12 40196 4 13 49483 5 14 54483 6 16 78073 7 18 99540 8 19 155646 9 21 125310 1. Estimate the regression equation Income = a + b(Education). 2. What is the predicted increase in Income for a one-year increase in Education? 3. What do you predict Income to be for a person who has 17 years of education? 4. How much of the variation in Income is explained (or accounted for) by Education?
Question 9 Assume a regression analysis yields a regression line with the value Y=$120,000 + $0.58X, where Y equals plant labor costs and X equals dollars of production output. If the company plans to produce $2,400,000 of product during the upcoming month, it would project plant labor costs to be: a. $324,000 b. $120,000 c. $204,000 d. $2,400,000
question 26 What is the relationship between the number of minutes per day a woman spends talking on the phone and the woman's weight? The time on the phone and weight for 8 women are shown in the table below. Time 54 88 82 61 39 40 84 83 Pounds 149 198 184 166 142 140 170 163 The equation of the linear regression line is: ˆyy^ = ?+ x (Please show your answers to 3 decimal places) Use the model to predict the weight of a woman who spends 50 minutes on the phone.Weight = ? (Please round your answer to the nearest whole number.) Interpret the slope of the regression line in the context of the question: For every additional minute women spend on the phone, they tend to weigh on averge 0.87 additional pounds. As x goes up, y goes up. The slope has no practical meaning since you cannot predict a women's weight. Interpret the y-intercept in the context of the question: The y-intercept has no practical meaning for this study. The average woman's weight is…
Questions 1-30 refer to the following scenario: A company reports bi-annual (twice a year) sales data. The sales data for the last three years is shown in below Table. Why would it be a bad idea to use the linear regression line to make forecasts when looking at the data? a The linear trend line does not capture the fact that, on average, sales go up. b The linear trend line does not capture the seasonality of the data. c Both a. and b. are correct. d None of the above. Decomposition forecasting decomposes data into which two factors? a Slope and intercept b Trend and seasonality c Past and future data d Decom and position In decomposition forecasting, the calculated seasonal index for the first bi-annual period is a 0.71 b 1.29 c 0.89 d 1.41 In decomposition forecasting, the calculated seasonal index for the second bi-annual period is a 0.89 b 0.71 c 1.41 d 1.29 Using only the regression line, the…

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