I need a help plz

can I get the answers to these questions plz

Problem 1: The American Society for quality (ASQ) conducted a salary survey of all its members. ASQ members work in all areas of manufacturing and service-related institutions, with a common theme of an interest in quality. For the U.S. survey, e-mails were sent to 70227 members, and 9449 valid response were received. The two most common jobs titles were managers and quality engineers. Another title is Master Black Belt, who is a person who takes a leadership role as the keeper of the six sigma process. An additional title is Inspector. Descriptive Statistics concerning salaries for these four titles are given below. Compare the salaries of inspectors, managers, quality engineers, and Master Black belts. Title Sample size Minimum Maximum Mean Median 110000 205516 174000 Standard deviation 17197 24109 18796 Inspector Manager Quality engineers Master Black belts 154 2228 1387 21000 27000 20000 45949 85551 72824 41800 83500 70000 134 33000 185250 24738 113385 114100 Problem 2: The file Protein contains calories, protein and cholesterol of popular protein foods (fresh red meats, poultry, and fish) Food beef, ground,extra lean beef, ground, regular beef round brisket flank steak lamb leg roast lamb loin chop, broiled liver, fried pork loin roast sirloin spareribs veal cutlet fried veal rib roast chicken with skin roasted chicked no skin roast Turkey, light meat clams cod flounder mackerel ocean perch salmon scallops shrimps tuna Calories Protein %calories fat % of calaories saturated fat cholestral 250 287 184 263 244 191 215 217 240 208 397 183 175 25 23 28 28 28 28 30 27 27 30 29 33 26 58 66 24 54 51 38 42 36 52 37 67 42 37 23 26 12 21 22 16 17 12 18 15 27 20 15 82 87 82 91 71 89 94 482 90 89 121 127 131 239 190 157 98 98 99 199 110 182 112 116 181 27 29 30 16 22 21 27 23 27 23 24 32 51 37 18 6 8 12 77 13 24 8 15 41 14 10 6 0 1 2 20 3 5 1 2 10 88 89 69 39 74 54 100 53 93 56 156 48 a. Compute the correlation coefficient between calories and protein. b. Compute the correlation coefficient between calories and cholesterol. c. Compute the correlation coefficient between protein and cholesterol. d. Based on the results of (a) through (c), what conclusions can you reach concerning calories, protein, and cholesterol? Problem 3: Define the following key terms: 1) 2) 3) 4) Ordinary least Squares regression ( OLSR ) Standardized Normal Random Variable 4 Assumptions of Regression Homoscedasticity Problem 4: Two Investments (A and B) have the following returns for the specified events: Events 1 2 3 Security A 10 5 -20 Security B 0 2 -10 Event Probability 0.5 0.4 0.1 Calculate the variances ( VA and VB ) and Standard deviations ( SA and SB ) , the Covariance (CAB ) and the correlation ( RAB ) Problem 5: You want to develop a model to predict the assessed value of house, based on heating area. The data in House 2 shows a sample of 15 single family houses in a city is selected. The assessed value (in thousands of dollars) and the heating area of the houses (in thousands of square feet) are recorded. (Hint: First determine which are the independent and dependent variables) a. Construct a scatter plot and , assuming a linear relationship, use the least square method to compute the regression coefficients b0 and b1 b. Interpret the meaning of the Y intercept, b 0 and the slope b1 in this problem c. Use the prediction line developed in (a) to predict the assessed value for a house whose heating area is 1750 sq. feet. d. Determine the coefficients of determination, R2 and interpret its meaning in this problem. Assesse d value 184.4 177.4 175.7 185.9 179.1 170.4 175.8 185.9 178.5 179.2 186.7 179.3 174.5 183.8 176.8 Heating Area 2 1.71 1.45 1.76 1.93 1.2 1.55 1.93 1.59 1.5 1.9 1.39 1.54 1.89 1.59 Age 3.42 11.5 8.33 0 7.42 32 16 2 1.75 2.75 0 0 12.58 2.75 7.17 Problem 6: Given a standardized normal distribution ( with mean 0 and a standard deviation of 1 ) , What is the probability that (i) (ii) (iii) (iv) Problem 7: Z is between -1.57 and 1.84 Z is less than -1.57 or greater than 1.84 What is the value of Z if only 2.5% of all possible Z values are larger? Between what two values of Z (Symmetrically distributed around the mean) will be 68.26 % of all possible Z values be contained? Given a standardized normal distribution ( with mean 0 and a standard deviation of 1 ), determine the following probabilities. i) P(Z 1.08 ) ii) P( Z -0.21) iii) P( -1.96 Z -0.21) iv) What is the value of Z if only 15.87% of all possible Z values are larger? Problem 8: Given a normal distribution with 50=4 . What is the probability that i) ii) iii) iv) X 43 X 42 5% of the values are less than what x value Between what two X values (symmetrically distributed around the mean)are 60% of the values. Problem 9: An industrial sewing machine uses ball bearings that are targeted to have a diameter of 0.75 inch. The specification limits under which the ball bearing can operate are 0.74 inch (lower) and 0.76 inch (upper). Past experience has indicated that the actual diameter of the ball bearings is approximately normally distributed with a mean of 0.753 inch and a standard deviation of 0.004 inch. What is the probability that a ball bearing is i) Between the target and the actual mean? ii) Between the lower specification limit and the target? iii) Above the upper specification limit? iv) Below the lower specification limit? v) Of all the ball bearings, 93% of the diameters are greater than what value? Problem 10: ^ Fitting a straight line yields the following prediction line: Y i=2+5 X i i) ii) iii) Interpret the meaning of Y intercept Interpret the meaning of slope Predict the value of Y for |X = 3 Problem 11: If the value of X in problem 5, ranges from 2 to 25. Should you use this model to predict the mean value of Y when X equals? i) 3 ii) -3 iii) 0 iv) 24 Problem 12: The marketing manager of a large supermarket chain would like to determine the effect of shelf space on the sales of pet food. A random sample of 12 equal-sized stores is selected with the following results: ( stored in Petfood) Calculate and interpret the meaning of the slope b 1, in this problem. STORE 1 2 3 4 5 6 SHELF SPACE, X (FEET) 5 5 5 10 10 10 WEEKLY SALES, Y (HUNDREDS OF DOLLARS) 1.6 2.2 1.4 1.9 2.4 2.6 STORE 7 8 9 10 11 12 SHELF SPACE, X (FEET) 15 15 15 20 20 20 WEEKLY SALES, Y (HUNDREDS OF DOLLARS) 2.3 2.7 2.8 2.6 2.9 3.1 Problem 13: An agent for a residential real estate company in a large city would like to be able to predict the monthly rental cost for apartments, based on the size of an apartment, as defined by square footage. The agent selects a sample of 25 apartments in a particular residential neighborhood and gathers the data below (stored in Rent). i) Calculate and interpret the meaning of b0 and b1 in this problem. ii) Why would it not be appropriate to use the model to predict the monthly rent for apartments that have 500 square feet? APARTMENT 1 2 3 4 5 6 7 8 9 10 11 12 13 MONTHLY RENT ($) 950 1,600 1,200 1,500 950 1,700 1,650 935 875 1,150 1,400 1,650 2,300 SIZE (SQUARE FEET) 850 1,450 1,085 1,232 718 1,485 1,136 726 700 956 1,100 1,285 1,985 APARTMENT 14 15 16 17 18 19 20 21 22 23 24 25 MONTHLY RENT ($) 1,800 1,400 1,450 1,100 1,700 1,200 1,150 1,600 1,650 1,200 800 1,750 SIZE (SQUARE FEET) 1,369 1,175 1,225 1,245 1,259 1,150 896 1,361 1,040 755 1,000 1,200 Problem 14: I. II. III. How do you interpret a coefficient of determination, r2, equal to 0.80? What is the interpretation of the Y-intercept and the slope in the simple linear regression equation? What is the interpretation of the coefficient of determination? Problem 15 : You want to develop a model to predict the selling price of homes based on assessed value. A sample of 30 recently sold single-family houses in a small city is selected to study the relationship between selling price (in thousands of dollars) and assessed value (in thousands of dollars). The houses in the city were reassessed at full value one year prior to the study. The results are in House1. i) ii) iii) Price($00 0) 194.10 201.90 188.65 215.50 187.50 172.00 191.50 213.90 169.34 196.90 196.00 161.90 193.00 209.50 193.75 206.70 181.50 194.50 169.00 196.90 186.50 197.90 183.00 197.30 200.80 197.90 190.50 197.00 192.00 195.90 Calculate and Interpret the meaning of the Y intercept, b0, and the slope, b1 in this problem. Use the prediction line developed in (i) to predict the selling price for a house whose assessed value is $170,000. Determine the coefficient of determination, r2, and interpret its meaning in this problem. Assessed Value 178.17 180.24 174.03 186.31 175.22 165.54 172.43 185.61 160.80 181.88 179.11 159.93 175.27 185.88 176.64 184.36 172.94 176.50 166.28 179.74 172.78 177.90 174.31 179.85 184.78 181.61 174.92 179.98 177.96 179.07 Typ e 0 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 0 1 0 1 0 1 0 0 0 1 0 1 0 Tim e 10 10 11 2 5 4 17 13 6 5 7 4 11 10 17 12 5 14 1 3 14 12 11 12 2 6 12 4 9 12