32The manager of a company is interested in studying the effect ofprice (X) on the sales (Y) of a new product. He selected four similarstores in the chain and measured demand in a two-week period at each ofthe four prices - Rs. 20, Rs. 30, Rs.40. and Rs. 50. The total demand ateach of these prices is given in the following table :PriceRs. 20Rs. 30Rs. 40Rs. 50Demand1381118447a. Find the least square regression line for this problem.b. Find the coefficient of determination.

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Answered: The manager of a company is interested…

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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| 1. We assume that the measured quantity y is a linear function of three independent variables, x1, x2, and x3, i.e., y = b + a1x1 + a2x2 + a3x3 Nine data points are measured by setting three levels for each parameter, and the data are placed into a simple data array as shown to Table 1, Calculate the y intercept and the three slopes simultaneously, one slope for each independent variable x1, x2, and x3. Table 1 data array X1 0 0 0 Questions -1 -1 -1 1 1 1 x ₂ 0 -1 1 0 -1 1 0 -1 1 X3 0 0 -1 -1 1 1 0 -1 1 y 10.472 7.253 14.708 9.861 4.374 10.339 12.082 10.678 14.519
Year, x 2009, 0 2010, 1 2011, 2 2012, 3 2013, 4 2014, 5 Average Monthly Cost to Workers for Family Health Insurance $296 338 348 362 384 404
The below data represent the advertising expenditure and sales of Liu Liu Ltd from year 2010 to 2019. Table: Advertising Expenditure vs. Sales from 2010 to 2019 Year Advert. Expenditure Sales Growth rate of Ad. Expenditure Growth rate of Sales (x, £000) (y, £000) 2010 19 84 2011 22 108 18% 29% 2012 32 82 46% -24% 2013 42 86 32% 6% 2014 47 84 11% -3% 2015 46 94 -2% 11% 2016 58 103 27% 10% 2017 63 115 8% 12% 2018 70 106 11% -8% 2019 80 142 15% 34% Total 479 1004 a. What is the statistical relationship shown in the table and list out 3 characteristics of this relationship?
In agricultural economics, a “supply response function” describes how supply of a commodity responds to changes in the price of that commodity. Year Seeded Area (hectares) Harvested Area (hectares) Average Yield (kg per ha) Production (tonnes) Price per Tonne ($/tonne) 1966 1,317,000 1,317,000 1,630 2,150,000 65 1967 1,424,000 1,424,000 1,720 2,449,000 60 1968 1,376,000 1,376,000 1,800 2,477,000 48 1969 1,012,000 1,012,000 1,720 1,742,000 46 1970 567,000 567,000 1,465 830,000 52 1971 1,019,000 1,019,000 1,975 2,014,000 50 1972 1,052,000 1,052,000 1,785 1,878,000 68 1973 1,214,000 1,214,000 1,725 2,095,000 158 1974 1,133,000 1,133,000 1,415 1,605,000 147 1975 1,255,000 1,255,000 1,690 2,123,000 130 1976 1,538,000 1,538,000 1,820 2,803,000 103 1977 1,295,000 1,295,000 2,125 2,749,000 98 1978 1,376,000 1,376,000 2,055 2,831,000 133 1979 1,214,000 1,214,000 1,680 2,041,000 170 1980 1,336,000 1,336,000 1,425 1,905,000 203 1981 1,577,900 1,577,900…
rials eo uations ow....zip Process . @ 2 D W Yaster Outfitters manufactures and sells extreme-cold sleeping bags. The table below shows the price-demand and total cost data, where: D • p is the wholesale price (in dollars) of a sleeping bag for a weekly demand of a sleeping bags; . C is the total cost (in dollars) of producing a sleeping bags. x (sleeping bags) p ($) 95 240 235 155 50 120 180 220 Use this data to create regression models to answer all of the questions below. #3 Price Model Find a quadratic regression equation for the price-demand data, using a as the independent variable. E p=a+bx+cr² Round a to the nearest integer, round b to 2 decimal places, and round c to 4 decimal places. Use this price model to answer question 1-2 below. $ 4 5 6 MacBook Pro C ($) 13,000 14,300 18,500 21,000 Y PEAR & * 8 ( 9 0 0 P A
Obtain the regression of demand on price from the following data and estimat demand when the price is 7.5 Price Demand Price Demand 2.0 75 4.5 40 2.5 60 5.0 40 3.0 55 5.5 35 3.5 50 6.0 30 4.0 45 6.5 30
The cost C (in dollars) of producing x units and the revenue R (in dollars) obtained by selling x units are shown in the figure. y 300 000 R 250 000 200 000 150000 100 000 50 000 X 1000 2000 3000 4000 5000
A biscuit production manager wants to investigate the linear relationship between the number of items produced and the production cost. To pursue his/her objective, the manager recorded the data on the number of items produced per day and the production cost per day (in thousands of Malaysian Ringgit) as shown in Table 3 Table 3 Days 1 2 4 6. 7 8 10 11 Number of items, (x) 26 67 86 17 10 44 53 40 77 80 20 20 Production cost, (y) 21 60 69 47 50 | 98 39 55 85 104 37 20 a) Name the TWo (2) most popular correlation coefficients. OPearson Oz-table OT-table Ochi-square Ospearman b) Calculate the correlation coefficient, r. ху x2 y2 26 21 676 44 60 2640 1936 3600 53 69 3657 2809 4761 47 1880 2209 77 50 3850 5929 2500 80 98 7840 6400 9604 20 39 780 400 1521 20 55 1100 400 3025 67 5695 4489 7225 86 104 8944 7396 10816 17 37 629 289 1369 10 20 200 100 400 E y² = Σχ ΣΥ- Σχy- Ex? = || || **Answer should be up to 4 decimal place (dp) r = c) Based on the correlation coefficient, robtained, make a…
The demand functions for two products are given below. P₁, P2, 91, and 92 are the prices (in dollars) and quantities for products 1 and 2. 91 = 1200 - 2p1 - 5p2 92 = 1300 - 5p1 - 3p2 What is the quantity demanded for each when the price for product 1 is $30 per item and the price for product 2 is $10 per item? Demand for product 1: Demand for product 2: Are these two products complementary goods or substitute goods? Select an answer
For the past 10 years, you have been observing the sales of your company since you embarked on an aggressive advertising campaign. You have been recording the amounts spent on advertising and the corresponding sales as follows: Year Advert (X) Sales (Y) 2001 10 44 2002 9 40 2003 11 42 2004 12 46 2005 11 48 2006 12 52 2007 13 54 2008 13 58 2009 14 56 2010 15 60 You would like to determine whether a relationship exists between your two variables of interest and therefore decide to run a regression. Specify the estimation model you will use (the econometric form). Identify the independent and the dependent variables. Use an econometric package of your choice (EViews, SPSS, STATA etc) to estimate the model. From the regression output, report the coefficients, standard errors, t-statistics, probability and R-squared (report the results in a table). Re-write the specified model in (a) with values…

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