X15.52.58332.98946.55.55653.5X2315567503871305642736044503955X310812781212585nn6mm1010X4869161517810416712644Y79.3200.1163.2200.1146177.730.9291.9160339.4159.686.3237.5107.2155 To answer item 17 to 23, use MS Excel data analysisConsider the following data set, in which a maker of asphalt shingles isinterested in the relationship between sales for a particular year andfactors that influence sales.X1 - promotional accounts; X3 - Competing brandsX2 - Active accounts; X4 - Potential and Y - sales in thousands of $If Promotional accounts is 10.2; Active accounts is 61; Competingbrands is 9; and Potential is 11.What would be the sales?sample answer. (2 decimal places)111.02

Given: X1=promotional accountsX2=Active accountsX3=Competing brandsX4=PotentialY=sales in thousands…

Answered: To answer item 17 to 23, use MS Excel…

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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The energy consumption of a group of studio condos is stated below. One set of condos is facing the sun while the other set is not. Determine the interquartile range in each data set and analyze the differences in context. Be sure to show all equations and calculations used. Answer in complete sentences. Facing the sun (kilowatt hours per month): 850, 700, 450, 320, 900, 1500, 456, 742, 753, 854, 1000, 547, 865 Not facing the sun (kilowatt hours per month): 950, 750, 525, 410, 990, 1640, 556, 842, 853, 954, 1150, 647, 965
The electric power consumed each month by a chemical plant is thought to be related to the average ambient temperature x1, the number of days in the month x2, the average product purity x3, and the tons of product produced x4. The past year's historical data are available and are presented in the following table. y 240 236 290 274 214 301 316 910 300 300 296 x1 25 31 45 60 00 65 99 72 14 X₂ 24 21 24 25 20 25 20 26 20 25 20 80 90 84 267 75 276 60 288 50 261 38 23 25 24 24 25 25 X3 91 90 88 00 87 91 94 2 87 DA 86 20 88 91 90 89 X₁ 100 95 110 88 94 99 97 96 110 105 100 98 Predict power consumption for a month in which x₁ =75°F, x₂ =24 days, x3 =90%, andx4 = 98 tons. Use 1 decimal place.
A. Find the ZSTAT B. What is the p-value
Please answer the question in the photo. Thank you!
The electric power consumed each month by a chemical plant is thought to be related to the average ambient temperature (x₁), the number of days in the month (x₂), the average product purity (x3), and the tons of product produced (x4). The past year's historical data are available and are presented in the following table: Y 240 236 270 274 301 316 270 296 267 276 288 261 25 31 45 60 65 72 80 84 75 60 50 38 X2 24 21 24 25 25 26 25 25 24 25 25 23 Fit a multiple linear regression to predict power (y) using x1, X2 X3, and X4. Calculate R2 for this model. Round your answer to 3 decimal places. 91 90 88 87 91 94 87 86 88 91 90 89 X4 100 95 110 88 94 99 97 96 110 105 100 98
Each year forbes ranks the world’s most valuable brands. A portion of the data for 82 ofthe brands in the 2013 forbes list is shown in Table 2.12 (forbes website, february, 2014).The data set includes the following variables:brand: The name of the brand.Industry: The type of industry associated with the brand, labeled Automotive& Luxury, Consumer Packaged Goods, financial Services, Other, Technology.brand Value ($ billions): A measure of the brand’s value in billions of dollarsdeveloped by forbes based on a variety of financial information about the brand.1-Yr Value Change (%): The percentage change in the value of the brand over theprevious year.brand Revenue ($ billions): The total revenue in billions of dollars for the brand.a. Prepare a crosstabulation of the data on Industry (rows) and brand Value ($ billions).Use classes of 0–10, 10–20, 20–30, 30–40, 40–50, and 50–60 for brand Value($ billions).b. Prepare a frequency distribution for the data on Industry.
2. A real estate expert wanted to find the relationship between the sale price of houses and various characteristics of the houses. He collected data on five variables, recorded in the table, for 12 houses that were sold recently. The five variables are: Sale price of a house in thousands of dollars. Size of the lot in acres. Price: Lot Size: Living Area: Living area in square feet. Age: Type of house: town house (T) or Villa (V) Age of a house in years. Price Lot Size Living Area Age Туре of house 255 1.4 2500 8 T 178 0.9 2250 12 T T. T. T. 263 1.8 2900 5 0.7 2.6 127 1800 24 305 3200 10 164 1.2 2400 18 245 2.1 2700 T V 146 1.1 2050 28 287 2.8 2850 13 V 189 1.6 2600 9 211 1.7 2300 8 V 123 0.5 1700 11 V a) Find the regression equation for the town house b) Find the regression equation for the Villa c) What is the price of a town house with a lot size of 1.3, living area of 1800, and is 7 years old? EBBBBE A> >}>l> >{
Answer the circled ones
For the following line graph, State at least one type of conclusion that could be made from the data provided for the KPI. Suggest one other data source that would provide more information to make data-driven decisions. Explain what information that source would supply. (I used the clustered bar chart) Examples: 1) The completion KPI measures the percentage of South Texas College students who finished their degree or certificate program within six years. From the line graph, it can be seen that the completion rates have remained nearly constant through the years. Data from the THECB was used in this case. 2) Information about the percentage of students who receive credit for attempted courses is available on the South Texas College KPI webpage. According to the data, over the past five years, an increasing number of students have been receiving credit for courses they have attempted. Data on the percentage of students who complete courses, the percentage of students who withdraw…
State if these variables would be expected to have a positive, negative, or no association. A. Number of schools and population size. o Positive Negative No association B. Income and number of health conditions. Positive o Negative No association C. Time spent meditating and stress level. Positive o Negative No association D. Number of pets and amount of money spent on printer ink. Positive Negative o No association E. Life expectancy and death rate. Positive Negative o No association
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