0111 .0160 .0719 0 -.0039 .0221 .0679 1 -.0452 .0396 .0519 1 .0980 .0291 .0390 0 .0089 .0293 .0351 1 -.0924 .0251 .0395 0 .0392 -.0104 .2645 1. Presents the report of Fisher’s linear discriminant function (classification function coefficient) 2. To show a hit rate

0111 .0160 .0719 0 -.0039 .0221 .0679 1 -.0452 .0396 .0519 1 .0980 .0291 .0390 0 .0089 .0293 .0351 1 -.0924 .0251 .0395 0 .0392 -.0104 .2645 1. Presents the report of Fisher’s linear discriminant function (classification function coefficient) 2. To show a hit rate

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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2. Use the graph below to answer the items that follow: pain_data$sex -0- female male A В C Treatment Type a. Is there a main effect of Sex? Explain b. Is there a main effect of Treatment? Explain. c. Is there an interaction between Sex and Treatment? Explain. Time (in minutes) 15 25 35 07
Calculate the coefficient of correlation between X and Y series from the followving data Series 15 15 No. of pairs of observations Arithmetic mean 25 18 3.01 3.03 Standard deviation 136 138 Sum of squares of deviations from mean Summation of product deviations of X and Y series from their respective arithmetic means = 12.
Example 13: Information regarding two mutual funds and marks inder ara given below: Standard Deviation (Per cent) Return Fund (Per cent) Beta Franklin Temple ton 14 30 0.72 Standard Chart 32 70 1.33 Market Index 20 48 1.00 Assuming the risk-free return as 5 per cent. Calculate the differential return (Jensen Ratio) for the two funds.
8. a quantile regression where cumulative abnormal returns are regressed on earnings surprises, if the coefficient is 0.1 for the 10th percentile, and 0.5 for the 90th percentile, then it means that the dispersion of returns (measured by the difference between 10th percentile and 90th percentile) increases with earnings surprises. True of False, why?
14. A water quality lab tests for the bacterial count in drinking water in a certain northern city. a) A test is made of a claim in the literature that the time to equilibrium in bacterial growth is greater in northerly climates, the standard deviation remaining unaffected. The mean time in southerly cities has been found, from many measurements, to equal 24.1 hours with a standard deviation of 2.3 hours. The northern lab tests 21 water specimens and finds the mean time to equilibrium bacterial growth is 25.4 hours, with an estimated stan- dard deviation of 2.2 hours, which is not significantly different from the standard deviation of 2.3 hours quoted above. Does this data bear out the claim in the literature about the increase in mean time to equilibrium, at the 5% level of significance? b) Two salesmen turn up at the laboratory one week, each claiming that the additive he is selling will decrease the time to equilibrium bacterial growth, compared to the other salesman's product. The…
150 30 0 www ii) 14 i) 21 ii) 28 days 35 42 (a) The Figure above shows simulated data on the number of people (in thousands) who use public transport every day in Melbourne. i) 49 56 Does this time series exhibit a time trend? If there is a time trend, explain what could explain this trend. (b) Consider the following estimated model of industrial production (Y), which uses quarterly data from 2001q1 to 2022q4 and includes a constant, a time trend (t) and 3 seasonal dummy variables (Q1, Q2, and Q3) in the equation. Does this time series exhibit seasonality? What statistical test could you perform to test whether there is seasonality? If there is seasonality, explain what could explain this seasonal pattern. log(Y) = 6.01 +0.05t -0.0201 -0.03Q2 -0.0103 Interpret the estimated coefficients for the time trend and three seasonal dummies. Based on the estimated equation above, what would be the forecast for industrial production for the first quarter of 2024?
Fall 2020 Yi_sales Xi_advert Xi*Yi Xi2 Predicted Yi_sales Residuals Residuals2 700 8 5600 64 626.6307 31.36929 984.0326 560 2 1120 4 292.4166 267.5834 71600.89 1000 13 13000 169 892.8691 47.13091 2221.323 302 5.8 1751.6 33.64 513.3378 -166.338 27668.26 100 2.1 210 4.41 303.7459 -179.746 32308.57 21681.6 275.05 134783.1 y-bar = 532.4 x-bar = 6.18 Correlation matrix Fall 2020 Yi_sales Xi_advert Yi_sales 1 0.816882 Xi_advert 0.816882 1 Suppose you are doing a simple, ordinary least squares regression to predict the sales (in millions) of a Fortune 500 company, Yi,…. using advertising expenditures (in millions), Xi. Above are some calculations completed. Which of the following statements best represents how to interpret the sample coefficient of correlation, r, for the sales…
Item y х^2 y^2 ху 0.51 8.34 0.2601 69.5556 4.2534 2 1.31 8.28 1.7161 68.5584 10.8468 2.34 16.2 5.4756 262.44 37.908 4 3.42 18.41 11.6964 338.9281 62.9622 5 4.18 27.32 17.4724 746.3824 114.1976 6 5.17 27.53 26.7289 757.9009 142.3301 7 6.47 31.85 41.8609 1014.4225 206.0695 8 7.46 32.78 55.6516 1074.5284 244.5388 8.56 37.59 73.2736 1413.0081 321.7704 10 9.76 51.93 95.2576 2696.7249 506.8368 sum 49.18 260.23 329.3932 8442.4493 1651.7136 average 4.918 26.023 32.93932 844.24493 165.17136 A= 4.25 B = 5.13 Standard Dev. 3.00 Is there a problem with this formula? If so, give the correct formula to find A and B. A = (7) [(x)(v)-(xy)" (x)²-(x)2 [(x)(xy)-(x)²(v) (x)²-(x)2 B = 3.
Beachcomber Ltd in a local car dealership that sells used and new vehicles. The manager of the company wants to know how different variables affect the sales of his vehicles. A random sample of yearly data was taken with the view to testing the model: SALES=a+BAGE+YMIL+SENG Where SALES= amount that a vehicle is sold for($000's), AGE = age of the vehicle, MIL= the total mileage of the vehicle at the point of sale and ENG = the size of the engine. The sample of data was processed using MINITAB and the following is an extract of the output obtained:
Quarterly sales of PnP (in hundred million Rands) from 2000 to 2004 are given in the table below. Q1 Q2 5 2000 2001 2002 2003 2004 B. 7.67 6 OC. 6.33 OD. 12.67 10 13 14 6 8 9 10 16 Q3 8 9 12 15 15 Q4 9 11 10 10 What is the three period moving average for 2002_Q2 (round off to two decimals). OA. 10.33 14
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5. The following table includes the information about a monthly time series. Month Time Series January 21 February 17 March 19 April 24 When the exponential smoothing method with a = 0.1 is applied, what is the mean absolute deviation (MAD)? A. 20.796. В. 9.16. С. 3.05. D. 3.56. 6. The following table includes the information about a monthly time series. Month Time Series January 21 February 17 March 19 April 24 When the exponential smoothing method with a = 0.5 is applied, what is the mean square error (MSE)? A. 21.5. В. 25. С. 41. D. 13.67.