Determine factors that have the greatest impact on property prices utilizing the introductory statistics concepts by choosing and visually representing relevant data, make valid predictions based on the analysis, and use statistics to craft an informed response to the question “What drives property prices?” Use confidence intervals and hypothesis tests to determine the validity of the mean property prices stated by two opposing parties in a disagreement over the affordability of housing in a given area. Make a recommendation regarding the affordability of housing based on the findings.
The dataset used for this assignment contains data from properties listed on “domain.com.au” in the Melbourne metropolitan area between January 2016 and September 2017. This data includes key geographic information (e.g., suburb, distance from Melbourne CBD), property information (e.g., property type, number of rooms, land area), and selling information (e.g., sale date, price).
Instructions Part 1.1 | Part 1.2 | Part 1.3 | Part 1.4 | Part 1.5 | Part 2 |
Create a copy of the Google Sheet, Property Prices – Part 1 Data Template (opens in new window). Information from 2,355 total properties have been included for you and you will use the pre-labeled tabs to perform pertinent computations for each part of the assignment.
Rename your copy of the Google Sheet using your username, and share your copy of the spreadsheet with your professor.
To fairly easily generate a random sample, use the Power Tools Add on >> Select Shuffle >> then select “Rows”.
There may be a short wait time while the add-on randomly shuffles the rows. Once the rows are shuffled, use the first 300 rows as your random sample (A2:A301). You must delete cells A301:A2356.
Copy the Google Slides – Part 1 Template (opens in new window) to use as a template for presenting your work for Part 1 of the assignment.
Rename your copy of the Google Slides file using your username.
Submit your copy of the Property Prices and Google Slides for grading.
discussion
hi i need help with two discussion and i have to reply to classmates. Please cite it from the powerpoint I upload as a attachment. discussion #1: Discuss how the coefficient of determination and the coefficient of correlation are related and how they are used in regression analysis.
What are some of the problems and drawbacks of the moving average forecasting model?
What effect does the value of the smoothing constant have on the weight given to the past forecast and the past observed value? replies #1: Discuss how the coefficient of determination and the correlation coefficient are related and how they are used in regression analysis.
The coefficient of determination is the proportion of the variability in Y explained by the regression equation. This is represented by R2 and usually between 0 and 1. In contrast, the coefficient of correlation is An expression of the strength of the linear relationship. It is always between -1 and 1 and is represented by r. The correlation coefficient can be positive, negative, or do not correlate. In regression analysis, r^2 tells us how the variable measured on the scatter plot directly affects the strength of r (correlation coefficient).
What are some of the problems and drawbacks of the moving average forecasting model?
The moving average requires a minimum of three months or periods of data to create the moving average forecast. Therefore, a moving average forecasting model cannot be used for a short time series of fewer than 3 months. Another major drawback is that the moving average uses past data to indicate trends but does not account for the current/potential changes. The moving average can indicate changes to the tend, but it is poor in predicting future changes, especially, when dealing with qualitative factors.
What effect does the value of the smoothing constant have on the weight given to the past forecast and the past observed value?
0 ? ? ? 1 is the smoothing constant. It gives past forecasts and past observed values more or less weight in the recent trend. The goal is to select the smoothing constant that produces the smallest MAD.
Source: Chapter 5 slides 28, 29, 31 and Ch4 22-26 replies #2: The coefficient of correlation, represented by R, measures the linear relationship between two variables, and the coefficient of determination is the sqaured of the coefficinet of correlation – R square – measures the explained sum of squares / explained variation in the model.
Some of the main drawbacks of moving average forecasting model are:
1. It overlooks complex relationships mentioned in the data
2. it does not respond to fluctuations that take place for a reason. For eg. cyclical fluctuations, seasonal fluctuations etx.
3. The trends obtained by moving average is neither a straight line nor a standard curve.
The value of the smoothing constant given to the current forecast is – a (alpha), and that given to the paat forecast is (1-a) i.e. 1 – alpha
1- alpha is also called the damping factor. A larger smoothing constant (a) gives a large weight to the value from the current unit time period and little value to the previous/past forecast.
The lower the value of alpha, the less movement in the time series i.e. the more smooth the series will become.
Citation: Quantitative Analysis for Management Thirteenth Edition. discussion #2: Why wouldn’t a company always store large quantities of inventory to eliminate shortages and stockouts?
When using safety stock, how is the standard deviation of demand during the lead time calculated if daily demand is normally distributed but lead time is constant? How is it calculated if daily demand is constant but lead time is normally distributed? How is it calculated if both daily demand and lead time are normally distributed?
Please help with problem stats
Statistics Assignment Help This problem set assignment will involve activities designed to solidify the concepts learned in both Modules One and Two. Problems will be similar to those you will face on the quiz in Module Four and will include one or two real-world applications to prepare you to think like a biostatistician.Check the module resource list to see which videos on the StatCrunch Help channel will help with this assignment.For support on the concepts of descriptive statistics, variables and sampling, visit the suggest Khan Academy videos in the module resources list.To complete this assignment, review the Module Two Problem Set Word Document document.
Videos: StatCrunch HelpThese videos offer help with different aspects of StatCrunch. Review them to see which ones may be helpful for the problem set assignment in this module.
Video Creating Frequency Tables With StatCrunch (cc) (3:43)
Video Creating Stem-and-Leaf Plots With StatCrunch (cc) (4:27)
Video Creating Dot Plots With StatCrunch (cc) (1:55)
Video Creating Histograms With StatCrunch (cc) (3:45)
Statistical Data Benefit
Part 1: Statistical Terms This week the discussion centered around how statistics are used in the criminal justice field, as well as what common statistical processes are and how they are calculated.
Complete this worksheet in which you describe the benefits of using statistical data in criminal justice. Provide an explanation and example of how each of the following are used in the Criminal Justice field
Statistical Process
Explain how it is Calculated
Example of when used in Criminal Justice
Mean
Median
Mode
Sample
Population
Part 2: Inferential and Descriptive Statistics
In 90 to 175 words, differentiate between when inferential and descriptive statistics are used in criminal justice. Provide examples to support your points.