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- Based on these data, multiple regression model equations can be obtained to predict per capita consumption which is influenced by paper consumption, fish consumption and fuel oil consumption. From Microsoft Excel processing, the following data are obtained: Question : a. Make a multiple regression equation model!b. How is the hypothesis testing based on each independent variable!c. Based on the answer to b how to model the enhanced multiple regression equationSuppose we obtain data on prices of big-screen televisions and estimate the following model: In(Price) = 4.06 + 0.06 * Size +0.23 * Wide + 0.34 * Plasma + 0.21 * LCD+0.09 * Memory where the dependent variable has been transformed, Size is the screen size measured in inches, Wide is a dummy variable equal to one if the television is a widescreen, Plasma is a dummy variable equal to one if the television is a Plasma screen, LCD is equal to one if the television is an LCD screen, and Memory is a dummy variable equal to one if the television has any memory card slots. What is the estimated price of a 42" Widescreen Plasma television with 2 memory card slots? Select one: O a. 7.24 O b. 7.33 1525.38 O d. 1394.09 • e 1881.83 Clear my choiceThe attached cost-output data were obtained as part of a study of the economies of scale in operating a charter high school in Wisconsin:What type of cost-output relationship (linear, quadratic, cubic) is suggested by these statistical results?
- A retail company wants to understand the factors that impact its sales revenue. The company has collected data on the following variables for the past 5 months: Total sales revenue (Y), Average store foot traffic (XI), and Marketing budget (X3). Develop a multiple linear regression model to predict that, what is the impact of average store foot traffic, and marketing budget on total sales revenue for the retail company. The data is summarized in Table 4. Table 4 Average Store foot traffic (X1) Sales Revenue (Y) 50$ 5 130S 45$ 48$ 7 6 120$ 200$ 60$ 708 5 4 Marketing Budget (X2) 130$ 250$ Note: Average Store foot traffic is the average number of people enter in the store per minute3. Consider the following regression model: Weekly Hours = Bo + B1 × Wage + uj Weekly Hours is the average number of hours the individual worked over the course of the year and Wage is the individual's average hourly wage over the course of the year. A researcher who collects data and regresses Weekly Hours against Wage finds that B1 > 0. The OLS estimator, B, however, likely suffers from omitted variable bias because those individuals who earn high wages may be driven personalities who would work long hours no matter the wage. Because of this omitted variable bias, it is likely the case that B1_B1. A) В)1ee 19 20 21 22 23 24 25 Price ars) QUESTION 6 A medical rescarcher found a significant relationship among a person's age x, chloresterol level x2, sodium level of the blood x3, and systolic blood pressure y. The regression equation is 97.3+0.700x+21&-299x3, Predict the systolic blood pressure of someone who is 31 years old and has a chloresterol level of 195 milligrares per deciliter and a sodium blood level of 142 milliequivalents per liter. O 171 O-24 O 128 O 85,087 QUESTION 7 MacBook Air SO DII F3 FS F6 & 4 W R T Y D H K V
- Seedlings of understory trees in mature tropical rainforests must survive and grow using intermittent flecks of sunlight. How does the length of exposure to these flecks of sunlight (fleck duration) affect growth? Researchers experimentally irradiated seedlings of the Southeast Asian rainforest tree with flecks of light of varying duration while maintaining the same total irradiance to all the seedlings. Below is the data. Fit a linear model to the data. Tree Mean Fleck (min) Relative growth rate (mm/mm/week 1 3.4 0.013 2 3.2 0.008 3 3 0.007 4 2.7 0.005 5 2.8 0.003 6 3.2 0.003 7 2.2 0.005 8 2.2 0.003 9 2.4 0 10 4.4 0.009 11 5.1 0.01 12 6.3 0.009 13 7.3 0.009 14 6 0.016 15 5.9 0.025 16 7.1 0.021 17 8.8 0.024 18 7.4 0.019 19 7.5 0.016 20 7.5 0.014 21 7.9 0.014 a)What is the rate of change in relative growth…The Pilot Pen Company has decided to use 15 test markets to examine the sensitivity of demand for its new product to various prices, as shown in the following table. Advertising effort was identical in each market. Each market had approximately the same level of business activity and population.a. Using a linear regression model, estimate the demand function for Pilot’s new pen.b. Evaluate this model by computing the coefficient of determination and by performing a t-test of the significance of the price variable.c. What is the price elasticity of demand at a price of 50 cents? TEST MARKET PRICE CHARGED (¢) QUANTITY SOLD(THOUSANDS OF PENS) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 50 50 55 55 60 60 65 65 70 70¢ 80 80 90¢ 90 40 20.0 21.0 19.0 19.5 20.5 19.0 16.0 15.0 14.5 15.5 13.0 14.0 11.5 11.0 17.0Linear regression is a highly effective data analysis method that accurately estimates the value of unknown data using related and known data. This is achieved using a linear equation that accurately models the quantitative relationship between the dependent (unknown) and independent (known) variables. In other words, by looking at how one variable affects another, this method can accurately forecast the value of the dependent variable. It involves choosing the variable to forecast and using another variable to make informed predictions about its value. In experiments, the independent variable is the cause, and its value remains constant while other variables have been modified. On the other hand, the dependent variable is the effect, and changes influence its value in the independent variable. As I began my business, I struggled with determining the appropriate pricing for my products. It was important that the prices were reasonable while allowing for a profit. The pricing had to…
- The weight (in pounds) and height (in inches) for a child were measured every few months over a two-year period. The results are displayed in the scatterplot. The equation ŷ = 17.4 + 0.5x is called the least-squares regression line because it is least able to make accurate predictions for the data. makes the strongest association between weight and height. minimizes the sum of the squared distances from the actual y-value to the predicted y-value. maximizes the sum of the squared distances from the actual y-value to the predicted y-value.An experiment is conducted to see the effect of light intensity on plant growth, what is the dependent variable in this scenario?3. Wine Participant magazine has collected average price per bottle for the prestigious Chateau Le Thundebird bordeaux for different vintages (years). The data appears in the table below. year of bottling price a) draw the scatter diagram showing how wine price varies by vintage year b) use the most appropriate regression equation to determine the relationship between year of bottling (age) and price. c) what is the explanatory power (RSQ) of that equation d) determine the predicted price of a bottle of this wine for the 2017 vintage. 2009 36 2010 40 2011 51 2012 60 2013 68 2014 72 2015 70 2016 65 2018 51 2019 44 2020 39