oose which answers are correct Which of the following method is a deterministic method of Reliability Assessment ? a . LO
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- A real estate developer studying the business problem of estimating the consumption of heating oil (gallons per month) has decided to examine the effect of atmospheric temperature (Fahrenheit degree) and the amount of attic insulation (inches) on the oil consumption. Data are collected from a random sample of 15 houses for the moth of January. The regression results for a quadratic regression model, Y = Bo + B1X1 + B₂X2 + B3X3 + €, is as follows, at the a = 0.05 level of significance. Where X₁ is the temperature variable and X2 is the insulation amount variable. The translated squared insulation amount predictor is X3 = X2/1. Source. df Sum of Squares Mean Squares F stat F crit 3.5874 3 ? Regression Error 11 Total 14 Variable Intercept Temperature Insulation Insulation² 229643.1645 6492.0649 236135.2293 ? ? Coefficient St. Error Lower 95% 624.5864 42.4352 -5.3626 0.3171 -44.5868 14.9547 1.8667 1.1238 531.1872 ? ? ? Upper 95% 717.9856 ? ? ? R-Square= 0.9725, Adjusted R-Square= 0.9650,…Give a mathematical model to investigate the relationship between one independent variable and dependent variable. Carry out appropriate tests for the significance of the relationship between the data and state the conclusions. Please show the data that you have used and please also provide step by step analysis until you reach the conclusion.Consider a linear regression model that relates school expenditures and family background to student performance in Massachusetts using 224 school districts. The response variable is the mean score on the MCAS (Massachusetts Comprehensive Assessment System) exam given in May 1998 to 10th-graders. Four explanatory variables are used: (1) STR is the student-to-teacher ratio, (2) TSAL is the average teacher’s salary, (3) INC is the median household income, and (4) SGL is the percentage of single family households. The Excel Regression output for the sample regression equation is given below. (a) What proportion of the variation in MCAS score is explained by the explanatory variables? (b) At the 5% level, are the explanatory variables jointly significant in explaining MCAS score? Explain briefly. (c) At the 5% level, which variables are individually significant at predicting MCAS score? Explain briefly. (d) Suppose a second regression model (Model 2) was generated using only…
- Interpret the following three sets of data using scatter chart and regression analysis, using slope , intercept, Coefficient of Determiniation (R2) , regression data (using excel), and 95% confidence interval Generate and explain the derivation of insights using regression analysis and its associated visualization. Differentiate between the signals identified by business analytics and the noise that is inherent in the system. 1. C16 (number of cars on the sales lot) versus Y: C17 (cars sold per day) C16 5 10 20 8 4 6 12 15 C17 27 46 73 40 30 28 46 59Interpret the following three sets of data using scatter chart and regression analysis, using slope , intercept, Coefficient of Determiniation (R2) , regression data (using excel), and 95% confidence interval Generate and explain the derivation of insights using regression analysis and its associated visualization. Differentiate between the signals identified by business analytics and the noise that is inherent in the system. 1.C20 (annual number of training hours) versus Y: C21 (time to serve customers in minutes) C20 100 100 100 100 100 125 125 125 125 150 150 150 150 150 175 175 175 175 C21 21.8 21.9 21.7 21.6 21.7 21.7 21.4 21.5 21.4 21.9 21.8 21.8 21.6 21.5 21.9 21.7 21.8 21.4A national standard requires that public bridges over 20 feet in length must be inspected and rated every 2 years. The rating scale ranges from 0 (poorest rating) to 9 (highest rating). A group of engineers used a probabilistic model to forecast the inspection ratings of all major bridges in a city. For the year 2020, the engineers forecast that 6% of all major bridges in that city will have ratings of 4 or below. Complete parts a and b. a. Use the forecast to find the probability that in a random sample of 7 major bridges in the city, at least 3 will have an inspection rating of 4 or below in 2020. P(x≥3)=
- Interpret the following three sets of data using scatter chart and regression analysis, using slope , intercept, Coefficient of Determiniation (R2) , regression data (using excel), 95% confidence interval, Regression and P value ,Do I reject the null hypothesis. Generate and explain the derivation of insights using regression analysis and its associated visualization. Differentiate between the signals identified by business analytics and the noise that is inherent in the system. 1.C20 (annual number of training hours) versus Y: C21 (time to serve customers in minutes) C20 100 100 100 100 100 125 125 125 125 150 150 150 150 150 175 175 175 175 C21 21.8 21.9 21.7 21.6 21.7 21.7 21.4 21.5 21.4 21.9 21.8 21.8 21.6 21.5 21.9 21.7 21.8 21.4 check_circle Expert Answer thumb_up thumb_down Step 1 X:annual number of training hour and Y:time to serve customers in minutes Steps to construct scatter plot in…Nana AB. supplies a range of computer hardware and software to 2000 schools within a large municipal region of Spain. When Nana AB. won the contract the issue of customer service was considered to be central to the company being successful at the final bidding stage. The company has now requested that its customer service director creates a series of graphical representations of the data to illustrate customer satisfaction with the service. The following data has been collected during the last six months and measures the time to respond to the received complaint (days).52434661563832 87783496745423 5632861281325253 3445213142125321 437662127336712 7889261074782332 2621567891851512 1556452145262134 2812672324432565 238872178547679 The customer service director has analysed this data to create a grouped frequency table and plotted the histogram. From this he made a series of observations regarding the time to respond to customer complaints. He now wishes to extend the analysis to use…give handwritten answer of the question-Which one of the following is the most used model or calibration curve for a one-component system in quantitative analysis? Y = bo + b1X1 + b2X2 + error Y = bo + b1X + b1X2 + error Y = bo + b1X + error Y = bo + b1X1 + b2X2 + b3X1X2 + error
- Besides the Regression analysis, spatial analysis, and time series analysis, explain an additional alternative approach to conducting the analysis for explanatory variables and response variables for Meaghan’s post below about a peer-reviewed scientific study that is a strong example of data collection: Meaghan’s Post: The below study was completed by conservationists to see and better understand the movement of a specific marine mammal species. The study was done in the waterways of California. The subject is monitored using a random 1.8; mile movement. Results of this project are analyzed by scientists and researchers. The species is a very curious one which allowed for better care and gained interest before committing to it. Ten observation stations were used about 2.4 km per the intervals were measured at equal intervals throughout. This often feels very accurate because the evidence is supported by science and other similar publications that allow due to interact without being…A regional distributor of NIKE shoes is in the process of analyzing the factors that influence thedemand for the NIKE brand. The distributor hired an economist to conduct a study on the demand for this product. The economist collected quarterly time series data from 1986Q1 to 1991Q4 on the following variables:SALES Sales of NIKE shoesRPDI Real personal disposal incomeCONF Consumer confidence indexD2 Dummy variable for quarter 2D3 Dummy variable for quarter 3D4 Dummy variable for quarter 4Ordinary Least Squares was applied using sales as the dependent variable and real personal disposal income, consumer confidence index, dummy variable for quarter 2, dummy variable for quarter 3, and dummy variable for quarter 4 as independent variables. The table below shows the OLS output.Model 1: OLS, using observations 1986:1-1991:4 (T = 24)Dependent variable: SALESCoefficient Std. Error t-ratio p-valueconst −139.452 61.8421 −2.255 0.0368 **RPDI 1.56286 0.438492 3.564 0.0022 ***CONF 0.256247…
