Use completing square to find the solutions of the equations.
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Use completing square to find the solutions of the equations.
a. x^2+2x-2=0 b. x^2-6x=1
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San Diego Mesa College Week 5 Predicting Winnings for NASCAR Drivers Discussion
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A golf club manufacturer is trying to determine how the price of a set of clubs affects the demand for clubs. The attached ...
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Develop the following simple linear regression models to predict the sale price of a house based upon a 90% level of confidence.Write the regression equation for each model.
Sale price based upon square feet of living area.
Sale price based upon number of bedrooms.
Sale price based upon number of bathrooms.
Develop the following multiple linear regression models to predict the sale price of a house based upon a 90% level of confidence.Write the regression equation for each model.
Sale price based upon square feet of living area and number of bedrooms.
Sale price based upon square feet of living area and number of bathrooms.
Sale price based upon number of bedrooms and number of bathrooms.
Sale price based upon square feet of living area, number of bedrooms, and number of bathrooms.
Discuss the joint statistical significance of each of the preceding simple and multiple linear regression models at a 90% level of confidence and 95% level of confidence.
Discuss the individual statistical significance of the coefficient for each independent variable for each of the preceding simple and multiple linear regression models at a 90% level of confidence and 95% level of confidence.
Compare any of the preceding simple and multiple linear regression models that were found to be jointly and individually statistically significant at a 90% level of confidence and select the preferred regression model. Explain your selection using the appropriate regression statistics.
Interpret the coefficient for each independent variable (or variables) associated with your selected preferred regression model.
Using the preferred regression model, predict the sale price of a house with the following values for the independent variables: 3,000 square feet of living area, 3 bedrooms, and 2.5 bathrooms.(Hint: You should only use the values for those independent variables that are specifically associated with your selected preferred regression model.)
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Southern New Hampshire University B&K Real Estate Company Statistics Question
The B&K Real Estate Company sells homes and is currently serving the Southeast region. It has recently expanded to cover t ...
Southern New Hampshire University B&K Real Estate Company Statistics Question
The B&K Real Estate Company sells homes and is currently serving the Southeast region. It has recently expanded to cover the Northeast states. The B&K realtors are excited to now cover the entire East Coast and are working to prepare their southern agents to expand their reach to the Northeast.B&K has hired your company to analyze the Northeast home listing prices in order to give information to their agents about the mean listing price at 95% confidence. Your company offers two analysis packages: one based on a sample size of 100 listings, and another based on a sample size of 1,000 listings. Because there is an additional cost for data collection, your company charges more for the package with 1,000 listings than for the package with 100 listings.Sample size of 100 listings:95% confidence interval for the mean of the Northeast house listing price has a margin of error of $25,000Cost for service to B&K: $2,000Sample size of 1,000 listings:95% confidence interval for the mean of the Northeast house listing price has a margin of error of $5,000Cost for service to B&K: $10,000The B&K management team does not understand the tradeoff between confidence level, sample size, and margin of error. B&K would like you to come back with your recommendation of the sample size that would provide the sales agents with the best understanding of northeast home prices at the lowest cost for service to B&K.In other words, which option is preferable?Spending more on data collection and having a smaller margin of errorSpending less on data collection and having a larger margin of errorChoosing an option somewhere in the middleFor your initial post:Formulate a recommendation and write a confidence statement in the context of this scenario. For the purposes of writing your confidence statement, assume the sample mean house listing price is $310,000 for both packages. “I am [#] % confident the true mean . . . [in context].”Explain the factors that went into your recommendation, including a discussion of the margin of error
San Diego Mesa College Week 5 Predicting Winnings for NASCAR Drivers Discussion
Prior to beginning work on this discussion forum, watch the Week 5 Introduction (Links to an external site.) video, and re ...
San Diego Mesa College Week 5 Predicting Winnings for NASCAR Drivers Discussion
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Outdoor World buys plastic pools from a supplier with an invoice amount of $19,5
Outdoor World buys plastic pools from a supplier with an invoice amount of
$19,500. The terms of sale are 5/10, n/30. The ...
Outdoor World buys plastic pools from a supplier with an invoice amount of $19,5
Outdoor World buys plastic pools from a supplier with an invoice amount of
$19,500. The terms of sale are 5/10, n/30. The retailer sent a partial payment of
$8,700 on the discount date. What is the net amount still due?
Ashford University Golf Club Manufacturing Worksheet
A golf club manufacturer is trying to determine how the price of a set of clubs affects the demand for clubs. The attached ...
Ashford University Golf Club Manufacturing Worksheet
A golf club manufacturer is trying to determine how the price of a set of clubs affects the demand for clubs. The attached file contains the price of a set of clubs and the monthly sales.
Assume the only factor influencing monthly sales is price. Fit the following three curves to these data: linear (Y = a + bX), exponential (Y = abX), and multiplicative (Y = aXb). Which equation fits the data best?
Interpret your best-fitting equation.
Using the best-fitting equation, predict sales during a month in which the price is $470.
Provide a short description of the tools used and why this was the "best-fitting equation"
MBA 6300 case study, quantitative analysis
There are numerous variables that are believed to be predictors of housing prices, including living area (square feet), nu ...
MBA 6300 case study, quantitative analysis
There are numerous variables that are believed to be predictors of housing prices, including living area (square feet), number of bedrooms, and number of bathrooms.The data in the Case Study No. 2.xlsx file pertains to a random sample of houses located in a particular geographic area.
Develop the following simple linear regression models to predict the sale price of a house based upon a 90% level of confidence.Write the regression equation for each model.
Sale price based upon square feet of living area.
Sale price based upon number of bedrooms.
Sale price based upon number of bathrooms.
Develop the following multiple linear regression models to predict the sale price of a house based upon a 90% level of confidence.Write the regression equation for each model.
Sale price based upon square feet of living area and number of bedrooms.
Sale price based upon square feet of living area and number of bathrooms.
Sale price based upon number of bedrooms and number of bathrooms.
Sale price based upon square feet of living area, number of bedrooms, and number of bathrooms.
Discuss the joint statistical significance of each of the preceding simple and multiple linear regression models at a 90% level of confidence and 95% level of confidence.
Discuss the individual statistical significance of the coefficient for each independent variable for each of the preceding simple and multiple linear regression models at a 90% level of confidence and 95% level of confidence.
Compare any of the preceding simple and multiple linear regression models that were found to be jointly and individually statistically significant at a 90% level of confidence and select the preferred regression model. Explain your selection using the appropriate regression statistics.
Interpret the coefficient for each independent variable (or variables) associated with your selected preferred regression model.
Using the preferred regression model, predict the sale price of a house with the following values for the independent variables: 3,000 square feet of living area, 3 bedrooms, and 2.5 bathrooms.(Hint: You should only use the values for those independent variables that are specifically associated with your selected preferred regression model.)
Prepare a single Microsoft Excel file using a separate worksheet for each question
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