Question

Here are the things that are required in this project. 1. Do a primitive analysis of the Sale price data. Do not use any other variables as of yet. Analyze only Sale price. Summarize the data using various measures and develop a frequency distribution. Which measure of central tendency would you use? (1-2 paragraphs) 2. What factors influence the sale price? Do not use regression yet. Use any of the other methods discussed in the course to answer this question. For example, you may develop a few charts or use pivot tables (various configurations) to analyze the influence of other variables. Summarize your observations (1-2 paragraphs). 3. Build a (multi variable) regression model that predicts the sale price of an apartment unit. Present the regression output in your report. 4. Prepare a professional summary report (2-3 paragraphs) that presents the results of your regression analysis. Be sure to interpret some of the coefficients. 5. Prepare a technical summary of your regression analysis (2-3 paragraphs)

Answer 1

1)

As you know, the application of measures of central tendency is meaningful if it is only applied to a specific form of data. Initially we need to study the data limitation before choosing an appropriate measures of central tendency.  The following are the most popular measures of central tendency.

• Arithmetic Mean
• Median
• Mode
• Geometric Mean
• Harmonic Mean

2)

factors which influence sale price

Comparison

Very few buyers will buy the first house they see. Most buyers will look at a number of properties in a suburb, or neighbouring suburbs, and they will compare these homes to each other. If a property is overpriced, it will help other similar properties in the area to sell for a better price - while sitting on the market for ages. For this reason, it is very important that a property's asking price is determined after thorough research of the prices similar properties in the same area have sold for in the preceding year, as well as the prices similar properties are being marketed for at that point in time. A good agent will be able to present you with a Comparative Market Analysis, which is a reliable aid when one has to decide about the correct marketing price.

Position

Rather buy the best home you can afford in a good suburb than the best home in a less popular suburb. This advice you must have heard many times before. The proximity of schools, public transport, shops and churches may also lead a buyer to choose one suburb above another. Muslim buyers give preference to homes close to a mosque or a madressa, and is often willing to pay a premium for that. If your property is situated on a busy road, or even close to one, many buyers will have a problem with the noise levels, and, unfortunately, that makes the buyer's pool smaller - with a negative effect on the selling price.

Perception

Of course, the "fact" that one neighbourhood is better than another can be based on perception. The Oxford dictionary defines perception as an "intuitive recognition (of truth, aesthetic quality, etc)." What the buyer perceives, is not necessarily a fact, but it still influences his decision. For this reason, the selling prices of two similar homes on different sides of the same street, but in neighbouring suburbs, may differ - the one suburb being perceived as better. Many buyers also perceive the proximity of an open area such as a park as a potential security risk - again not necessarily true, but still a factor that influences their decision.

Condition

Buyers will pay more for a smaller house which is in tip-top condition than a rambling, run-down house across the road. The importance of maintaining a property can not be emphasised enough. If the buyer sees a few cracked windows and peeling facia boards as he walks in, it immediately makes him wary of other potential problems. Don't neglect maintenance until you want to sell - it is likely to be too costly to do all at once. If you attend to problems as they arise, you will benefit greatly when you sell in the future.

Accommodation

Obviously a home which offers a lot more accommodation will sell for more - if its position or condition does not negatively affect buyers' perception of its value. Most buyers looking for family home require at least 3 reception rooms, 3 to 4 bedrooms, 2 bathrooms (of which one must be main en suite), a kitchen and a covered entertainment area. Good staff accommodation also is in demand, as is a double garage. Cottages and home offices will have value only for a smaller group of buyers, and unless your buyer is in need of this, he is unlikely to pay more for the property because it has these "extras". Keep this in mind when extending your property. That jacuzzi room or built-in bar will only increase your property's value if the buyer has those on his wish list.

3)

If you've ever wondered how two or more pieces of data relate to each other (e.g. how GDP is impacted by changes in unemployment and inflation), or if you've ever had your boss ask you to create a forecast or analyze predictions based on relationships between variables, then learning regression analysis would be well worth your time.

In this article, you'll learn the basics of simple linear regression, sometimes called 'ordinary least squares' or OLS regression - a tool commonly used in forecasting and financial analysis. We will begin by learning the core principles of regression, first learning about covariance and correlation, and then moving on to building and interpreting a regression output. Popular business software such as Microsoft Excel can do all the regression calculations and outputs for you, but it is still important to learn the underlying mechanics.

Variables

At the heart of a regression model is the relationship between two different variables, called the dependent and independent variables. For instance, suppose you want to forecast sales for your company and you've concluded that your company's sales go up and down depending on changes in GDP.

The sales you are forecasting would be the dependent variable because their value "depends" on the value of GDP and the GDP would be the independent variable. You would then need to determine the strength of the relationship between these two variables in order to forecast sales. If GDP increases/decreases by 1%, how much will your sales increase or decrease?

Covariance

The formula to calculate the relationship between two variables is called covariance. This calculation shows you the direction of the relationship. If one variable increases and the other variable tends to also increase, the covariance would be positive. If one variable goes up and the other tends to go down, then the covariance would be negative.

The actual number you get from calculating this can be hard to interpret because it isn't standardized. A covariance of five, for instance, can be interpreted as a positive relationship, but the strength of the relationship can only be said to be stronger than if the number was four or weaker than if the number was six.

Correlation Coefficient

Covry Grrelation-h"Com

We need to standardize the covariance in order to allow us to better interpret and use it in forecasting, and the result is the correlation calculation. The correlation calculation simply takes the covariance and divides it by the product of the standard deviation of the two variables. This will bind the correlation between a value of -1 and +1.

A correlation of +1 can be interpreted to suggest that both variables move perfectly positively with each other and a -1 implies they are perfectly negatively correlated. In our previous example, if the correlation is +1 and the GDP increases by 1%, then sales would increase by 1%. If the correlation is -1, a 1% increase in GDP would result in a 1% decrease in sales - the exact opposite.

Regression Equation

Now that we know how the relative relationship between the two variables is calculated, we can develop a regression equation to forecast or predict the variable we desire. Below is the formula for a simple linear regression. The "y" is the value we are trying to forecast, the "b" is the slope of the regression line, the "x" is the value of our independent value, and the "a" represents the y-intercept. The regression equation simply describes the relationship between the dependent variable (y) and the independent variable (x).

y=bx +0

The intercept, or "a," is the value of y (dependent variable) if the value of x (independent variable) is zero, and so is sometimes simply referred to as the 'constant.' So if there was no change in GDP, your company would still make some sales - this value, when the change in GDP is zero, is the intercept. Take a look at the graph below to see a graphical depiction of a regression equation. In this graph, there are only five data points represented by the five dots on the graph. Linear regression attempts to estimate a line that best fits the data (a line of best fit) and the equation of that line results in the regression equation.

als Foe ast 350 300 250 200 150 100 50 Y- 88.5 +34584 0.00 050 1.00 1.50 200 2.50 3.00 3