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ECON 940 – Statistics for Decision Making

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Statistics for Decision Making (ECON 940)

   

Added on  2020-05-04

ECON 940 – Statistics for Decision Making

   

Statistics for Decision Making (ECON 940)

   Added on 2020-05-04

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STUDENT IDCOURSE
ECON 940 – Statistics for Decision Making_1
INTRODUCTIONWe use the dataset of 60 observations given to us for 3 districts to answer a range of questions on prices across places and other features like ocean view, and type of dwelling-unit or house. The 3 districts covered are Sydney, Wollongong and Newcastle. 2 other categorical variables are provided for each data point- the type of dwelling can be unit or a house. We are also told about the absence or presence of ocean view with the dwelling. The focus of the report is on PRICES of dwellings and how these vary across regions, dwelling type and presence of an ocean view. We use Microsoft Excel to answer a range of queries pertaining to this data. We use concepts like measures of central tendency, dispersion, correlation, confidence intervals, and hypothesis testing. We use t distribution to deal with the hypothesis testing. Visual charts are included - pie chart, bar chart, and histogram to aid in our analysis. ANALYSIS:This section is divided into sub sections, where each subsection deals with a separate query. We note that we have 4 variables in all, out of which only 1 variable is quantitative. This is prices of dwellings. All other variables are categorical in nature. A.We begin with an analysis of prices irrespective of location, dwelling type and ocean view. A snapshot of prices in the following histogram is given. We have used 6 classes here with width of$150 each. This chart is based on the following data. We can see that prices are relatively normally distributed. This is all seen from the descriptive statistics given below. PRICEMean543.0481Standard Error24.64311
ECON 940 – Statistics for Decision Making_2
Median528.7699Standard Deviation190.8847Sample Variance36436.97Kurtosis0.844758Skewness0.770584< 300630-45014450-60021600-75011750-9004> 9004The mean price is $543, whereas the median is $528. So we have 50% dwellings with a price that exceeds $528. As mean exceeds median we know that the distribution is positively skewed, but not by a large degree. The skewness value is only 0.77.B.Next we disaggregate the data by location. Each location has 20 data points, which are analysed in table below. As can see that mean price is highest for Sydney. Variance in prices is also highest in Sydney, showing the highest dispersion in prices. The lowest average price is for Newcastle, which also has lowest dispersion value.To compare average against dispersion we use the CV- coefficient of variation value. It is given as the ratio of standard deviation to mean value. It is a relative measure of the dispersion. As shown the CV is highest for Newcastle, whereas it is lowest for Wollongong. This data is not in line with variance / standard deviation. The latter is a an absolute measure of dispersion, whereas CV is an absolute measure devoid of units. CV is therefore better measure to compare dispersion of different series. SYDNEYWOLLONGONGNEWCASTLEMean717.2859532.6064044379.252Standard Error38.7988824.3238852223.33847Median668.4485515.1707706364.8505Standard Deviation173.5139108.7797217104.3728Sample Variance30107.0611833.0278410893.69Kurtosis0.500424-0.4987024-0.52924Skewness0.9300830.2086336060.507136CV0.2419030.2042403560.275207A visual comparison is shown below. The mean, standard error, median and standard deviation are all highest for Sydney followed by Wollongong and then lowest for Newcastle.
ECON 940 – Statistics for Decision Making_3

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