answer 6 Problem: As a data analyst, you could use multiple linear regression to predict crop growth.You want to see how amount of rainfall, temperature, and amount of sunlight affects cropgrowth. An experiment with 20 sampling units was conducted obtaining the following data.Plot1234567891011121314151617181920Crop Growth(mm/hr)0.420.220.250.150.080.320.340.270.140.340.260.390.170.180.400.230.330.200.140.19Rainfall(inches)3.205.965.068.343.064.253.806.087.965.008.433.171.885.256.047.626.379.868.138.50Temperature(°C)3418222628132719233235303919409218161317209119Sunlight(hrs)1161059710111391041442592731. Identify the dependent and all independent variables.2. Give the multiple linear regression model, in general form, for this problem andlabel your variables.3. Compute for the partial regression coefficients, give the multiple linear regressionwith the coefficients and interpret the value of each coefficient.4. Test the significance of the multiple linear regression model.5. If the regression is significant in (4), identify which independent variable hassignificant contribution in describing the dependent variable.6. Compute for R² and Rådjand interpret your results.7. What would be the expected crop growth rate when the amount of rainfall is 0inches, amount of temperature is 37°C, and amount of sunlight is 12 hrs.?

We need to perform the multiple regression analysis on the given problem. n= 20 observation k=…

Answered: Problem: As a data analyst, you could…

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.
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.
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 $1,000 increase in the amount of money spent on advertising. How does this confidence interval relate to your answer to part a)?
Suppose you work for a software company and you would like to estimate the market value of your company. You hypothesize that the market value of a software company (response variable) is a linear function of its earnings (explanatory variable). To test this, you collect data on the earnings and market value of 4 companies in your industry. This results in the following data: Company earnings ($mm) market cap ($mm) Unicorn 12 70 Bolt 2 24 PsiCo 100 700 Big 50 450 You fit a linear regression to these data. What is the residual for PsiCo?

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