Concept explainers
(a)
Construct a
(b)
Find the value of
Find the value of
Find the value of b.
Find the equation of the least-squares line.
Construct the line on the scatter diagram.
(c)
Find the sample
Find the value of the coefficient of determination
Mention percentage of the variation in y is explained by the least-squares model.
(d)
Check whether the claim that the population
(e)
Find the number of people would be predicted to buy insurances when a week during which Dorothy makes 18 visits.
(f)
Verify the values of
(g)
Find the 95% confidence interval for the number of sales Dorothy would make in a week during which made 18 visits.
(h)
Check whether the claim that the slope
(i)
Find a 80% confidence interval for
Interpret the confidence interval.
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Chapter 9 Solutions
Understandable Statistics: Concepts and Methods
- You are given below the following information about advertising and sales. Adv. Exp. (X) (S Lakhs) Sales (Y) Lakhs) Мean 10 90 S.D. 3 12 Correlation Coefficient 0.8 (a) Calculate the two regression lines. (b) Find the likely sales when advertisement expenditure is (c) What should be the advertisement expenditure if the company wants to attain a sales target of 15 lakhs. 120 lakhs ?arrow_forwardThe following three problems refer to the data sets displayed below: b 11.If the slope of the least squares regression line is negative, what else must be negative? a) The correlation (r)? b) The coefficient of determination (r)? c) The y-intercept (bo) d) More than one of the above must be negative. e) None of the above need be negative.arrow_forward2.1 Mariana, a researcher at the World Health Organisation (WHO), collects information on weekly study hours (HOURS) and blood pressure when writing a test (BLOOD) from a sample of university students across the country, before running the regression BLOOD = f(STUDY). She collects data from 5 students as listed below: X (STUDY) 1 2 3 Y (BLOOD) 140 128 125 123 4 8 90 She replicates her study and collects data from 62 students from a rival university. She derives the residuals followed by computing skewness (S) equals 2.50 and kurtosis (K) equals 0.10 for the rival university data. Conduct the Jacque-Bera test of normality at a = 0.01.arrow_forward
- Suppose two variables are under study are temperature in degrees Fahrenheit (y) and temperature in degrees centigrade (x). The regression line for this situation is y = 9/5X +32. Assuming there is no error in observing temperature, What is the expected correlation coefficient in this given scenario. Answer:arrow_forwardA doctor collects data on all the men in his practice. Their daily potassium intake is 4500 mg/day, with a standard deviation of 1000 mg/day. They have an average systolic blood pressure of 150 mmHg, with a standard deviation of 10 mmHg. The two variables have correlation r = 3. -0.4. (a) One of the men is at the 25 percentile of potassium intake and 75 percentile of blood pressure. Relative to all other men at the 25th percentile potassium intake, this man's blood pressure is (circle one) smaller than average about average larger than average Justify your answer using a written explanation and/or a diagram.arrow_forward1) The ratio of the regression mean square to the error mean square is: (a)Usually assumed to be 0.05 (b)The correlation coefficient (c)The covariance (d)The F ratio 2)The coefficient of determination, R2, ... a. Decreases as higher polynomial terms are included in the model b.Can be greater than 1.0 for transformed variables c.Measures the slope of the regression line d.None of these answers is correct 114 3. Assume you have experimental data that could be analyzed using a paired t-test or an unpaired t-test. If there are 22 degrees of freedom for the unpaired t-test, which value below describes the degrees of freedom for the paired t-test?arrow_forward
- 7. Battery life has a strong, negative, linear relationship with temperature. If the least-squares regression line using xx = temperature explains 90% of the variation in battery life, which of the following must be the correlation, rr, between battery life and temperature? (A) -0.90 (B) 0.90 (C) -0.95 (D) 0.95 (E) Cannot be determined without the original data. Submit Answerarrow_forward3. The equations of two regression lines obtained in a correlation analysis of 60 observations are 5x = 6y + 24 and 1000y = 76x – 3708. What is the correlation coefficient and what is its probable error ? Show that the ratio of the coefficient of variability of x to that of y is 24arrow_forward2. Given the following sets of information, find the linear least squares regression and the correlation coefficient.arrow_forward
- 12) Use computer software to find the best multiple regression equation to explain the variation in the dependent variable, Y, in terms of the independent variables, X1, X2, X3. 9896 29.1 1 421 9680 42.3 2 653 10449 29.8 3 573 10811 26.0 4 CORRELATION COEFFICIENTS 546 10014 34.3 5 499 10293 22.7 6 %3D 60% 0.00 Y/ X2=0.280 Y/ X3 = 0.930 504 9413 24.2 7 %3D 611 9860 31.6 8 %3D 646 9782 25.6 9 789 12139 37.9 10 COEFFICIENTS OF DETERMINATION 773 12166 33.9 11 YI X1 = 0.259 Y/ X2 = 0.079 YI X3 = 0.864 Y/ X1, X3 = 0.880 YI X1, X2, X3 = 0.884 753 9976 37.4 12 %3D 852 10645 27.0 13 %3D 755 9738 31.5 14 %3D 815 9933 39.9 15 %3D 902 10132 25.3 16 986 11145 30.4 17 909 9775 32.7 18 945 9549 35.0 19 866 10077 33.8 20 1178 11550 29.4 21 1230 10600 37.1 22 1207 11280 42.9 23 968 12100 32.2 24 1118 12420 30.5 25 A) Î = 57.8+0.036X, +28.1X3 B) Ý = -21.1+0.36X, +2.62X, +27.6X3 %3D C) Ý = 201.7+0.40X, +22.3X3 D) Y = 308.6+ 29.9X3 %3Darrow_forward8. Areal estate company wants to study the relationship between Home price (in OMR 1000) and the following variables: Floor size (in 100 square feet) (X 1 ) Type of Heating (X 2 =1 if electricity and X 2 =0 , Gas, Location of home in town and X 3 =0 , if in village). Use this to answer the following questions: Then the P-value for testing Ho: B2=0 against H 1 :B 2 not =0 is A. 0.2623 B. 0.0073 C. 0.0037 D. 0.1312 E. Nonearrow_forward5. A higher-education consultant wanted to see whether a university computer help desk had sufficient resources to assist faculty with computer problems. The consultant randomly sampled 200 faculty members. To determine the factors that influence waiting time, he estimated a simple regression model and got the following results: Wâit = 45 – 7Staff (50) (3.2) n=200 R2= 0.850 (standard errors of the coefficients are in parentheses beneath the coefficient estimates) where Wait = waiting time in minutes and Staff = the number of staff working at the help desk. a. Clearly explain, in words, the meaning of the following statistics: coefficient estimates, standard errors of the coefficients, R2. b. Test, at the 1% significance level, the hypothesis that the number of staff has no effect on waiting time. Perform a two-sided test.arrow_forward
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