Question

2. Please use 2 places of decimal for calculations. A. The following are a list of quantities of skirts sold weekly in the past ten weeks at & Co: 100, 125, 200, 100, 127, 300, 99, 200, 215, 225. Graph a histogram/boxplot to show these data. Explain/show how you get the numbers for the boxplot/histogram. B. The table below gives the prices of skirts at Jonathan & Co. and quantities sold in selected weeks. Calculate the mean and the standard deviation of the Quantity sold using the numbers in the table. Date Quantity sold Price Week 1 100 Week 5127 Week 6225 C. The mean price is $14 and the standard deviation of price is $7. Using the 3 pairs of prices and quantities in the table above, calculate r, the correlation coefficient between the price and quantity sold. D. Calculate “a” and “b” in the best straight line Predicted Quantity = a + b Price. Do you think that the line would give you a good prediction of the quantity sold at a price of $30? Explain. E. Explain the difference between the correlation coefficient, r, and the slope coeffient, b, using this example.

Answer 1

Topic Date Gwnhil t ry Iso 150 201 252 Quarties sold

tisto grom is drown after arranging the number in ascending ader. Then took difference of macinand minimum which was divided by no of classes or jaterials (which is taken as u here) = 300-99 5o. as often rooding oth SI comes the interval value therefore our intervab & corresponding frequences one Interval frequency 99 to 150 lso to 201 201 to 252 252 to 303 1 ☺ fx-) 2 y (8 5) 6x-x] [53] (95) 100 - 50.67 2567.11 21 7 -354.67 49 127 - 23-67 560.11 14 0 0 0 74.33 5125. un 7 -7 - 520.33 49 Total = 42 8652.67 X= 452 = 150.67 S= 14 Table - 1 - 875 8652.67 = 65.72

Ž (xi) (y ) 머 쳐 - IX – 875 65.7787 - - 0.9502 Quantity = atbpruce y = atba using regression b= {xibi x where x, = x Y = 4 therefore & here quantity is y f brick is x we know Exisi = – 876 Ext is summation from price data which is 96 as bor the calculation ( Please refer b=-875 9% = -8.93 a = 150 67 - 68.93×14 a =ý - 8 x - 150.17 + 125.02 - 275.69 formula

245.69 – 8.93 brice Preduled quantity = for price 30 - 275.69 - 8.93X3o = 779 it's not good prediction becaus we don't have crough cataboants to hit the regressim line. Corelation is used to find the linear relationship between two racicable in our case it is negaline 0.9502, which shows high Correlation withe 2 datę sets, whereas regression cofficient tell the variability in dependent variable due to independent variable. It tells the dependency of dependent variable and helps is predicting the value to dependent variable.