An engineering student wants to study the impact of temperature and humidity on yield. The following data was recorded by the student. S.N Temperature Humidity Yield 1 40 57 112 45 54 118 50 54 128 4 55 60 121 60 66 126 6 65 59 136 7 70 61 144 8 75 58 142 80 59 149 10 85 56 165 A: Simple Linear regression model between yield and temperature. a) Compute the correlation coefficient between yleld and temperature. oy Ad the equation of Tegressioun line between yielde on tomporaturo using least square method. c) Draw the scatter diagram between yield and temperature. d) Show the best fitted line on scatter diagram. e) What percentage of the variable yield can be explained by the variable temnerature?
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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 table displays the EPA fuel efficiency estimates (in miles per gallon) and the curbweight (in pounds) for a random sample of current year model vehicles.MPG 23 18 28 19 25 17 18 14Weight 3184 3598 2734 4082 2623 4685 4178 5488(a) Determine the linear regression model that will best predict the EPA fuel efficiency estimates(MPG) of a vehicle based on its curb weight.(b) How well does the linear regression model fit this sample data?(c) For a vehicle that weighs 4000 pounds, predict its EPA fuel efficiency estimate.A sales manager collected the following data on annual sales for new customer accounts and the number of years of experience for a sample of 10 salespersons. Salesperson Years of Experience Annual Sales ($1000s) 1 1 80 2 3 97 3 4 92 4 4 102 5 6 103 6 8 111 7 10 119 8 10 123 9 11 117 10 13 136 Develop a scatter diagram for these data with years of experience as the independent variable. Develop an estimated regression equation that can be used to predict annual sales given the years of experience. Use the estimated regression equation to predict annual sales for a salesperson with 9 years of experience.
- Consider the following computer output from a multiple regression analysis relating the price of a used car to the variables: age of car, mileage, and safety rating. Coefficients Coefficients Standard Error t� Stat P-value Intercept 38356.1138356.11 4686.294686.29 8.1858.185 0.00000.0000 Age (Year) −18219.29−18219.29 2196.312196.31 −8.295−8.295 0.00000.0000 Mileage(in Thousands) 1149.561149.56 1897.651897.65 0.6060.606 0.54720.5472 Safety Rating 1396.751396.75 159.64159.64 8.7498.749 0.00000.0000 Does the sign of the coefficient for the variable mileage make sense?The following table gives information on the amount of sugar (in grams) and the calorie count in one serving of a sample of varieties of Kellogg's cereal. Find the predictive regression equation of the number of calories on the amount of sugar. Sugar (grams) 6 15 12 11 8 6 7 4 9 14 20 13 3 Calories 120 200 150 110 120 80 190 120 120 190 190 120 120Consider the following computer output from a multiple regression analysis relating the price of a used car to the variables: age of car, mileage, and safety rating. Coefficients Coefficients Standard Error t� Stat P-value Intercept 42465.6942465.69 5320.545320.54 7.9817.981 0.00000.0000 Age (Year) −21096.02−21096.02 2551.522551.52 −8.268−8.268 0.00000.0000 Mileage(in Thousands) −1312.73−1312.73 103.02103.02 −12.743−12.743 0.00000.0000 Safety Rating 1533.821533.82 165.72165.72 9.2559.255 0.00000.0000 Does the sign of the coefficient for the variable safety rating make sense?
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- A study of the amount of rainfall and the quantity of air pollution removed produced the following data shown in table below: Daily Rainfall x (0.01 cm) Particulate Removed y (μg/m3) 7 126 7.9 129.3 7.5 125.3 9.2 120.2 10.8 116.7 5.8 119.2 5.6 138.7 2.7 147.5 9.2 110.3 Compute and interpret the coefficient of determination, and coefficient of correlation for the given data. What will be the regression equation, when swapped depended and independent variableA sixth-grade teacher believes that there is a relationship between his students’ IQscores (y) and the numbers of hours (x) they spend watching television each week. Thefollowing table shows a random sample of 7 sixth-grade students.y 125 116 97 114 85 107 105x 5 10 30 16 41 28 21 Does the data provide sufficient evidence to indicate that the simple linear regressionmodel is appropriate to describe the relationship between x and y? Perform a model utilitytest at α = 0.05. (Give H0, Ha, rejection region, observed test statistic, P-value, decisionand conclusion.)Find the Pearson sample correlation coefficient between x and y. Then interpretthe result.A mail-order business selling personal computer supplies, software and hardware maintains a centralized warehouse. Management is currently examining the process of distribution from the warehouse and wants to study the factors that affect the warehouse distribution costs. Data collected over 24 random months contain the warehouse’s distribution cost (in thousands of Rands), the sales (in thousands of Rands) and the number of orders received. A multiple linear regression model was fitted to the data by using Stat1.2. Use the output to answer the questions that follow by typing only the letter of the correct option in the answer boxes. Variablesy: Warehouse Distribution Costx1: Salesx2: Number of Orders Model Fitting StatisticsR2 = 0.8504Adj R2: ? Regression Coefficients Beta Parameter Standard b Parameter Standard Estimates…