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- The following table provides values of the function f(x,y). However, because of potential; errors in measurement, the functional values may be slightly inaccurately. Using the statistical package included with a graphical calculator or spreadsheet and critical thinking skills, find the function f(x,y)=a+bx+cy that best estimate the table where a, b and c are integers. Hint: Do a linear regression on each column with the value of y fixed and then use these four regression equations to determine the coefficient c. x y 0 1 2 3 0 4.02 7.04 9.98 13.00 1 6.01 9.06 11.98 14.96 2 7.99 10.95 14.02 17.09 3 9.99 13.01 16.01 19.02If your graphing calculator is capable of computing a least-squares sinusoidal regression model, use it to find a second model for the data. Graph this new equation along with your first model. How do they compare?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?
- 29. Below is the result of running linear regression in R. The least-squares regression equation is Call: Lm(formula = variable_C ~ variable_W) Coefficients: (Intercept) -5.5162 variable_W 0.2666 A. variable_W -5.5162 + 0.2666 * variable C B. variable C = -5.5162 + 0.2666 * variable W C. variable W = 0.2666 – 5.5162 * variable C D. variable_C = 0.2666 – 5.5162 * variable_W B. bO. None of the above. MacBook Air DD 80 888 000 F3 F4 F6 F7 F8 F9 F5 # $ & 8If I add the additional condition which is the labor is female using the following: #People who is femalefemale = x*0.46 Will it become dependent variable and how will I do linear regression model by adding this condition?The relationship between the entrance exam score (x) of MBA students and their GPA (y) upon graduation is given by ŷ = -0. 57 +0. 013x Based on this model, the predicted GPA of a student who had 200 for the entrance exam score is (Keep 2 decimal places) and the value of the slope of this fitted regression line is (Keep all the decimal places)
- Select all the charts that violate the conditions for linear regression. These are residual plots so the red line represents the average residual, zero. 미 Od (a) (d) 매 Get help: Video (b) (e) (c) (1)I need help with part b please. linear regression equation: y_hat = 6.6500 + 1.7000x table of temperatures versus converted sugars: Temperature, x Converted Sugar, y 1 8.2 1.1 8.1 1.2 8.7 1.3 9.9 1.4 9.6 1.5 8.7 1.6 8.2 1.7 10.6 1.8 9.3 1.9 9.2 2 10.7Hormone replacement therapy (HRT) is thought to increase the risk of breast cancer. The accompanying data on x = percent of women using HRT and y = breast cancer incidence (cases per 100,000 women) for a region in Germany for 5 years appeared in the paper "Decline in Breast Cancer Incidence after Decrease in Utilization of Hormone Replacement Therapy." The authors of the paper used a simple linear regression model to describe the relationship between HRT use and breast cancer incidence. † HRT Use Breast Cancer Incidence 46.30 103.30 40.60 105.00 39.50 100.00 36.60 93.80 30.00 83.50 (a) What is the equation of the estimated regression line? (Round your numerical values to four decimal places.) ŷ = (b) What is the estimated average change in breast cancer incidence (in cases per 100,000 women) associated with a 1 percentage point increase in HRT use? (Round your answer to four decimal places.) cases per 100,000 women (c) What breast cancer…
- Here, mean of X is 3 and the mean of Y is 7. The regression line that predicts Y from X necessarily goes through the point (3,7). True FalseTom has been gathering data concerning the cost of a spa treatment, y', during the before Valentine's Day. The only independent variable that he has considered is the number of minutes, "x," in the treatment. Suppose Tom collects data on the relationship between the number of minutes in a treatment and the resulting cost of we the treatment. Tom finds that the correlation between cost and number of minutes is strong and positive. Therefore, he has performed a linear regression analysis on his data. His results are that the constant "a" is 35, and the coefficient "b1" for the independent variable is 1.3. Which of the following is the correct linear regression equation that would allow Tom to predict the cost of a spa treatment given the number of minutes? Oy = 78x + 35 Oy' = 1.3x + 35 %3D Oy = 78x - 1.3 %3D OY = -1.3x - 35The authors of the paper "Power-Load Prediction Based on Multiple Linear Regression Model"t were interested in predicting the load on the electric power system in China using data on y = Power consumption (in hundreds of millions of kwh), x, Population (in millions), and x, = Gross domestic %3D product (in billions of dollars), for 21 years. The model equation proposed in the paper is y = -113,527 + 0.974x, + 0.057x, + e. (a) According to this model, what is the mean power consumption (in hundreds of millions of kwh) for a year if the population was 160,000 million and the gross domestic product was 600,000 billion dollars? hundreds of millions of kwh (b) Interpret the value of B, in this model. When the [ gross domestic product v is fixed, the mean increase in [power consumption (in hundreds of millions of kwh) V associated with a 1-million unit increase in [population is 0.974.