Suppose X ~N (12, 32). Find the 90th percentile of X. To find your -- answer, pretend that the following table is (x) = P(Z≤ x), where Z~N (0, 1). (Note: these values do not agree with the actual normal tables! This artificial table is constructed so that you cannot find the answer in software or elsewhere.) Data table X (x) (x) Φ (π) 0.1 0.536 1.1 0.828 2.1 0.934 0.2 0.571 1.2 0.846 2.2 0.937 0.3 0.606 1.3 0.863 2.3 0.940 0.4 0.640 1.4 0.877 2.4 0.943 0.5 0.672 1.5 0.890 2.5 0.944

Given three data points (1,6), (3, 28), and (10, 231), it is found that the function y=2x2+3x+1 passes through all the three data points. Your estimate of y at x = 5.8 is

Use the data in the table below for Questions A - G. Remember to show ALL your work. A researcher performs a study investigating differences in perceived stress during the COVID-19 pandemic in undergraduate students. She measures levels of depression in a group of 7 first year undergraduates as well as in a group of 7 fourth year undergraduates using a continuous variable scale. The data are presented in the following table: First Year Fourth Year (X) (Y) 4 6. 10 10 9. 8 10 4 2 A. Calculate the mean for the first year group and the mode for the fourth year group В. In general, what is the purpose of obtaining a measure of central tendency? Calculate the range for the first year group. Describe one advantage and one disadvantage of the range as a measure of variability. С. D. Calculate the variance for each of the two separate groups Е. Calculate the standard deviation for each of the two separate groups F. Previous studies have shown that first years tend to have perceived levels of…

You work as a data scientist for a real estate company in a seaside resort town. Your boss has asked you to discover if it's possible to predict how much a home's distance from the water affects its selling price. You are going to collect a random sample of 9 recently sold homes in your town. You will note the distance each hame is from the water (denoted by X, in km) and each home's selling price (denoted by y, in hundreds of thousands of dollars). You will also note the product X-y oaf the distance from the water and selling price for each hame. (These products are written in the row labeled "Xy")- (a) Click an "Take Sample" to see the results for your random sample. Distance from the water, X 3.8 21 1.3 0.2 29 21 4.4 1.3 1.8 (In lam) Selling price, y (In hundreds of thousands of dollars) Tako Sample 5.7 10.3 17.1 14.8 8.2 1.4 6.1 10.4 9.6 xy 21.66 21.63 22.23 2.96 23.78 15.54 26.84 13.52 17.28 Sand data to calculator v Based on the data from your sample, enter the indicated values…

A researcher collected a data set for a random sample of 930 individuals living in and around London, with data collected over 1-year period. The Table below reports the OLS coefficient estimates and standard errors, where the dependent variable is [100xIn(well-being)]. Commuting time/60 -0.237 (0.039) -0.062 (0.008) 0.2 Age Age squared/100 Hours worked log real income Married or cohabiting Num. of children Saves Degree constant (0.010) -0.0053 (0.001) 0.0297 (0.009) Answer: 0.589 (0.032) -0.051 (0.015) 0.299 (0.022) -0.022 (0.035) 0.234 (0.052) The explanatory variables are: Commuting time/60 = Number of minutes of commuting time per day; Age= Age in years; Hours worked = Hours worked per week; Log of real household income = 100xLn(real household income measured in £10,000s); Num. of children = Number of children under the age of 18; Save regularly = 1 if save regularly, 0 otherwise; University degree = 1 if has a University degree, O otherwise. What is the predicted value of the…

Does the length of a surgery patient's stay in the hospital depend on the length of time the operation took? The table below gives the operative time (in hours) and the length of the hospital stay (in days) for 13 patients. (Comma separated lists of the data are also provided below the table to ease in copying the data to R.) Operative Time (In Hours) (x) Length of Hospital Stay (In Days) (y) 2 8. 2.3 7 2.1 7 2.1 2.2 10 2.3 7 1.3 3 2.5 7 7 1.8 6. 2.2 6. 1 11 1.8 x: 2, 2.3, 2.1, 2.1, 2.2, 2.3, 1.3, 2.5, 2, 1.8, 2.2, 1, 1.8 y: 8, 7,7, 5, 10, 7, 3, 7, 7, 6, 6, 11,6 1. Find the best fit line for the data. y = + 2. Graph the scatter plot of the data together with the best fit line using R. (This portion of the problem is not graded.) 3. If a patient's operation took 1 hour, how many days should we expect them to stay at the hospital?

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A trucking company tries to determine the factors affecting total daily traveling time for one of its driver (hours). X1 = distance traveled (miles) and X2 = the number of deliveries measured for a sample of 20 drivers. Data scientists tried a F test to determine if the total daily traveling time has a significant linear relationship with X1 and/or X2. If you know F value is 10, do you think if there is significant linear relationship? a = 0.05 O No, there is not significant linear relationship since p-value is in (0.1,0.5). O No, there is not significant linear relationship since p-value is in (0.05,0.1). O Yes, there is significant linear relationship since p-value is in (0.01,0.05). O Yes, there is significant linear relationship since p-value is in (0.001,0.01).

The relationship between sleep hours and overall happiness level on the next day was estimated as below in a linear format. (Happiness level is measured through a five-point scale: 1 = Extremely unhappy, 5 = Extremely happy)   Overall happiness level = 0.27 * Sleep hours + 2.34 (The p-value for the coefficient of Sleep hours is 0.02.)   Based on this equation, what is the expected happiness level of a person on a certain day, when he slept 7 hours the day before?

Suppose that for a typical FedEx package delivery, the cost of the shipment is a function of the weight of the package measured in ounces. You want to try to predict the cost of a typical shipment given package dimensions. If 10 packages in a city are sampled and the regression output is given below, report the regression equation. 1)  (cost of delivery) = 1.468*(weight) - 23.015   2)  (weight) = -23.015*(cost of delivery) + 1.468   3)  (cost of delivery) = -23.015*(weight) + 1.468   4)  (cost of delivery) = 1.468*(weight)   5)  (weight) = 1.468*(cost of delivery) - 23.015