Statistic Assignment

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  1. Question
  2. BE01106 - BUSINESS STATISTICS

                                                                     Assignment

    SEMESTER 1, 2017

    Due in Week 8, submit in class

    _________________________________________________________________________

    The BEO1106 assignment has a total of seven (7) tasks for students to complete.  Each task accounts for 10 marks.  The total marks available for the entire assignment is 70.   The assignment accounts for 35% of the overall assessment in the unit. So the total mark you receive for your assignment will be converted to a mark (out of 35) before being aggregated with your test and examination marks to produce your final result for the unit.

     

    The assignment consists of two parts.  Part I is to collect a set of sample data which will be used to answer six questions in Part II.  In Week 4, 5, 6 and 7, the Model assignment questions will be discussed during the seminar sessions.  This will assist you to understand the requirements for those questions.  Both Part I and II must be submitted together for correction due in the seminar of week 8.  Late submission will be penalised by 1 mark per day for a maximum of 5 working days. 

     

    The assignment must be submitted in hard copy.  An Assessment Declaration (the form can be downloaded from the Assessment Information page on the unit website) is required and must be stapled to the front of your assignment. To avoid any complications associated with misplaced assignments, make a copy of your assignment before you hand in the original to your seminar leader for correction. 

     

    Although we will discuss the assignment tasks in the seminars, you will need to complete the tasks in your own time.  For most students, Assignment Part I tasks can be completed within one hour on average. It is recommended that to start working on the assignment the earlier the better and do not leave it to the last minute. 

     

    Presentation

    • Your answers must be presented in task number order and be clearly labelled with the appropriate task number.
    • Your assignment must be presented in Microsoft (MS) Word. Copy and paste any relevant Excel outputs to this document immediately before (above) any relevant written answers to each task.
    • If you are unfamiliar with the use of the MS Word Equations Editor, you may write algebraic/mathematical/statistical symbols and notation in neat handwritten form.
    • Your answers must be clear. You must highlight relevant items on any required Excel outputs and make reference to them in your written answers.
    • When asked to perform a manual calculation (i.e. the use of MS Excel is not specified) you must show all working. This must include intermediate steps where relevant.  Failure to do so will result in a loss of marks.    
    • Completed assignments are to be presented for correction on A4 paper, stapled in the top left hand corner. You are permitted to print on both sides of the paper. Colour printing is recommended for graphs/charts.  If printed as greyscale, be mindful and creative to make the greyscales distinct shades.   
    • Do not submit the assignment with fancy bindings, folders or plastic envelopes.
    • Do not include the assignment questions nor the population property data with your submitted assignment.
    • You are permitted to consult reference textbooks and notes and to communicate with other students. However, the work you hand-in for correction must be your own.  Be aware that the University penalties for plagiarism are severe.

     

    Introduction

     

     

    The Assignment Data (PopulationPropertyData.xls) file, which you can access from the

    Assessment Information page on the unit website contains, in the range A1:I401, real estate sales data for a population of 400 properties around Melbourne in a particular week. You are required to select a random sample of 50 properties from this population. The variables in the data set are as follows:

    V1 = Region where property is located (1 = North, 2 = West, 3 = East,     4 = Central)

    V2 = Property type (0 = Unit, 1 = House)

    V3 = Sale result (1 = Sold at auction, 2 = Passed-in, 3 = Private sale, 4 = Sold before auction).  Note that a blank cell for this variable indicates that the property did not sell.

    V4 = Building type (1 = Brick, 2 = Brick veneer, 3 = Weatherboard, 4 = Vacant land)

    V5 = Number of rooms

    V6 = Land size (Square metres)

    V7 = Sold Price ($000s)

    V8 = Advertised Price ($000s)

    Column A (PN), contains the property identification numbers from 001 to 400 properties.

     

    Selecting your Random Sample and Creating  your Sample Data File

     

    To select your random sample, you need:

    • A printed copy of the Random Number Table handy.
    • Open the PopulationPropertyData.xls file on computer screen.
    • Create a SamplePropertyData Excel file and keep it open on computer screen.

     

    In order to select the sample data that will form the basis of your assignment you will need to make use of the random number table provided as a pdf file (RandomNumbers.pdf) on the Assessment Information page of the unit website. The provided table of random numbers is, as the title suggests, a sequence of randomly generated numerical digits (0 to 9).  These digits are arranged in a table with ten columns (numbered 0 to 9) and one hundred rows (numbered 01 to 00) spread over two pages.  The entries in each column of each row consist of six single digits.

     

    Your first task is to select 50 three-digit random (property) numbers ranging from 001 to 400 from the table of random numbers. The type of simple random sampling that we will be engaged in here is termed “without replacement” because we specifically do not want to allow a property number to be selected more than once.  If we allowed this to occur we would run the risk of the sample being biased and so not representative of the population.  In the population, a particular property only occurs once and so it would not do to allow a particular property to occur more than once in your sample.  In this way we can be more assured that the sample is typical of the population and so perform inferential statistical analyses about the population with some confidence.

     

    In order to select your 50 random property numbers you will need to first go to a starting position row and column in the random number table (Note ~ not the population property data) defined by the last three digits of your VU student identification number (the assignment marker will check your student ID number against the three digits number you use to collect the random sample). The last two digits of your VU ID number identifies the row and the third last digit identifies the column of your (relatively) “unique” starting position.

     

    For demonstration purposes, if the last three digits of your student identification were 7, 4 and 9 (i.e. 749), you would commence your property number selection at the starting position - row 49 and column 7 of the random number table. You are required to colour/highlight the starting row number 49 and the starting column number 7.  You should be able to see that the six digit number occupying that position is 217035.

     

    Then, moving across the row, from left to right from the starting position, examine the first three digits of each six digit number and then the second three digits in each of the columns of the table.  If any of these three digit numbers are between 001 to 400 inclusive, they are “good” numbers (the population data numbered from 001 to 400). Ignore any number greater than 400 or equal to 000. They are “not-good” numbers

     

    Continue reading across row 49 from left to right starting at column 7 as instructed, you would encounter the following three digit good numbers:

     

                                                    217, 035, 306, 150, …

     

    You need to record the first good property numbers, i.e. 217, and open the PopulationPropertyData.xls Excel file located on the Assessment Information page of the unit website.  On the spreadsheet, scroll down the PN column to locate 217 (note: do not select the Excel spreadsheet row number 217.  Select the row with 217 in the PN column).  At this row, highlight from 217 under the PN column across to the right up to the V8 column, use Cut and Paste procedure to cut the row of data and paste the data into a new Excel file (name it  and save it as SamplePropertyData.xlsx). Next is to repeat the Cut and Paste process for PN 035, and for PN 306 and the subsequent three digit good numbers selected from Random Number Table up to the point when the row of the spreadsheet in the SamplePropertyData file grown up to 50 rows of data. Make sure you copy the column headings, PN, V1, ... V8 into your sample data file as the heading for the columns.   

     

    Each time a number is selected from the Random Number Table, insert a strikethrough mark over the selected number on the Random Number Table to mark it off. It is possible that you may come across some three digit good numbers more than once (we call them “repeated” number).  The use of the Cut and Paste procedure is the “without replacement” sampling procedure to ensure that no repeated PN number and the corresponding data can be select more than once in this sample selection process. When a repeated number is found, colour/highlight/cross-out it in the Random Number Table to indicate that this good number has not been used to select the sample data (See the Assignment Part I Model Answers file).

     

    Note that if you reach to the end of Row 50 on the first page of the Random Number Table but still not yet to collect 50 good numbers, continue the process on to Row 51 on the top of the second page of the Random Number Table (as the same practice in the Assignment Part I Model Answer).  Similarly if you reach to the end of Row 00 on the second page, proceed on to row 01 on the top of the first page.  Once 50 good numbers are selected and the 50 rows of data have been copied from the PopulationPropertyData file into the SamplePropertyData file, this will form a completed sample data set occupying spreadsheet columns A to I and spreadsheet rows 1 to 51 (Refer to the Assignment Part I Model Answers file on the Assessment Information).

     

     

    Assignment Part I

     

    Part I of the assignment simply requires the submission of a hard copy of your sample property data presented in a maximum of no more than 3 printed pages in total.  (See the Assignment Part I Model Answer). This sample data set will form the basis of the statistical presentation and analysis tasks contained in Part II of the assignment.

     

     

    Task 1 ( 10 marks)

     

    (a)        Make a hard copy of your Random Number Table containing the following:

     

    (i)         The highlight of the starting row and starting column of the sample selection process.  (Refer to the Assignment Part I Model Answer). (1 mark)

     

    (ii)        The strikethrough/mark on the three digits good numbers and the cross-out of the repeated number(s). (Refer to the Assignment Part I Model Answer). (4 marks)

     

    (b)     Print a hard copy (see note below) of your sample property data (9 columns x 51 rows of data plus the column headings row) from the Excel file (SamplePropertyData) obtained per the above instructions.  (5 marks)

     

    Note:  when printing the hard copies in (b) please select Page Setup in Excel and then under Print in the Sheet window, tick the Gridlines and Row and column headings check boxes.  This will ensure that the Excel row and column headings are included in your Excel printout.  The screenshot for printing row and column heading is attached below for reference.

     

     

     

     

                END OF PART I OF THE ASSIGNMENT

     

     

     

    Assignment Part II   

    Answers to the six assignment tasks in Part II must be based on the sample data file that you have created in Part I. All tasks in this assignment require you to obtain an Excel output prior to performing some analysis. Copy and Paste these outputs to your assignment MS- Word document immediately preceding any subsequent analysis. Explanations must be precise and to the point. Charts and tables must have appropriate titles and numerical values must be rounded to an appropriate number of decimal places and accompanied by the correct units of measure.

     

     

    Task 2 (10 marks)

     

    Use Excel to produce a Frequency Column Chart (4 marks) and a Relative Frequency Pie-Chart (3 marks) for your sample to show the number and proportion, respectively, of each building type.

     

    Use these graphical summaries to answer the following questions:

     

    (a)        How many properties in your sample consist of brick buildings? (1 mark)

    (b)        Which building type occurs most frequently in your sample?  (1 mark)

    (c)        What proportion of properties in your sample consists of weatherboard buildings? (1

               mark)

     

     

    Task 3 (10 marks)

     

    • Use Excel to sort your sample “Sold Price” data and paste into your MS Word assignment document. (1 mark)
    • Use the percentile location formula;

                            , and the three associated rules (Slide 11 of Week 2 Seminar, Session 1) (1 mark) to determine:

     

    Remember to show all working!

    (i)         The 70th percentile. (1 mark)

    (ii)        The first and third quartiles. (2 mark)

     

    • Briefly explain what the 70th percentile that you have determined informs you about your sample “Sold Price” data. (2 mark)
    • Determine the Inter-Quartile Range of your sample “Sold Price” data and provide a brief explanation of what information this statistic provides about your sample data. (3 marks)

     

     

     

     

     

    Task 4 (10 marks)

     

    (a)        Use Excel to produce a Descriptive Statistics table for your sample “Sold Price” data        and paste into your MS Word assignment document. (4 marks)

    (b)        Use results from Task 3 to determine manually for this data, the upper and lower inner fence limits;

     

    Remember to show all working!

                            IFUL = Q3 + 1.5 x IQR   (1.5 marks)

     

                and      IFLL = Q1 – 1.5 x IQR   (1.5 marks)

     

     (c)       Based on the limits calculated in (b), choose from the numerical summary measures        provided in the Descriptive Statistics table, and/or measures calculated previously in          Task 3;

     

                (i)         an appropriate measure of central tendency, and, (1 mark)

                (ii)        an appropriate measure of dispersion for your sample “Sold Price” data. (1

                           mark)

     

                Provide a brief explanation of the reasoning behind your choice in both cases. (1

               mark)

     

     

    Task 5 (10 marks)

     

    Remember to show all working! Failure to do so will result in the loss of marks.

     

    • From the Descriptive Statistics table obtained in Task 5, identity three pieces of evidence that indicate whether your sample “Sold Price” data has been obtained from a normally distributed population or not. What is your conclusion?  Note:  Make sure only one piece of evidence relates to the shape of the sample data.   ( 2 marks)

     

    • Regardless of your conclusion in above, assume the “Sold Price” population data is normally distributed. Applying the Standard Normal tables, calculate how many “Sold Price” observations in your sample would expect to lie within 1.5 standard deviations of the mean (i.e. between z = –1.5 and z = +1.5).   (4 marks)

     

    • Use the mean and standard deviation from the Descriptive Statistics table of Task 5 to calculate the bound for 1.5 standard deviation spread from the mean. Using the “Sold Price” sample data, manually count the number of observations fall within the bound. State whether this count matches, approximately, your answer to (b) and hence whether this result confirms (or not) your conclusion in (a).  (4 marks)

     

     

     

     

    Task 6 (10 marks)

     

    Remember to show all working! Failure to do so will result in the loss of marks.

     

    • Use Excel to produce a Descriptive Statistics table for the “Sold Price” variable in your sample suitable for constructing an interval estimate of the population mean “Sold Price”. (2 marks) Hence determine:

    (i)         A point estimate of the mean Sold Price” of the population of properties.  (1 mark)

    (ii)        A 90% confidence interval estimate of the mean “Sold Price” of the population of properties. (2 marks)

    (iii)       Make a brief verbal statement explaining the meaning of the confidence interval estimate obtained in (ii) in the context of the variable in this task. (3 marks)

     

    (b)        If the population mean “Sold Price” is actually 650 ($000s), would you consider the interval estimate obtained in (a), to be satisfactory?  Explain why or why not. (2 marks)

     

     

     

    Task 7 (10 marks)

     

    Remember to show all working!  Failure to do so will result in the loss of marks.

     

    (a)       Use Excel to produce a Descriptive Statistics table for the brick veneer properties in your sample suitable for constructing an interval estimate of the population proportion of brick veneer properties.  Hence determine: (2 marks)

     

    (i)         A point estimate of the proportion of brick veneer properties in the population. (1 mark)

    (ii)        A 99% confidence interval estimate of the proportion of brick veneer properties in the population. (1 mark)

     

    (b)        Using the following formula:

     

    (sample statistic) ± (critical z or t) ´ (standard error of the sample statistic)

     

    Use the rule of thumb for good normal approximation (Slide 3 of Week 7 Session 2) for proportion, then the Empirical Rule (Slide 8 of Week 5 Session 1) for a Normal distribution to determine a 95% confidence interval estimate of the proportion of brick veneer properties in the population. (4 marks)

     

     

    (c)    Compare, in terms of the precision, the interval manually calculated in (b) with the interval obtained from the Descriptive Statistics table in (a).  Explain why the direction of the change in precision is expected.  (2 marks)

     

    END OF PART II OF THE ASSIGNMENT

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Subject Statistics Pages 16 Style APA
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Answer

Statistic Assignment

Task 1

Done and attached

Task 2

Figure 1: Frequency column for building type

Figure 2: Pie chart for building type.

Answers to the questions with reference to the visual aid presented for clarity (Frankfort-Nachmias, & Leon-Guerrero, 2015).

  1. How many properties in your sample consist of brick buildings?

In the sample brick building consists of 17 properties

  1. Which building type occurs most frequently in your sample?

The most frequently occurring building type in my sample was Brick Veneer at 23 properties representing 46%

  1. What proportion of properties in your sample consists of weatherboard buildings?

14% of the properties in the sample consist of weatherboard buildings

Task 3

  1. Sorted Sold price

Table 1: Sorted Sold price

125

375

435

552

1175

230

385

445

555

1215

270

387.5

453

610

1225

300

400

460

665

1606

319

400

461.25

670

1885

340

415

475

740

346

420.5

481.5

741

350

425

510

785.5

354

425

525

882.5

374

427

544.5

1126

 

  1. Using percentile formula
  2. Using the formula to find the 70th Percentile

Replace the values in the formula n=45, P=70

                         70th quartile =(45+1)*70/100=32.2

Rounding off to the nearest whole number we get 32 as the position of the 70th percentile which is $555000

  1. First and Third Quartiles

First Quartile = 25th Percentile, Third quartile =75th Percentile

Replacing the values we get the following

First Quartile =(45+1)*25/100=46*0.25=11.5

Rounding off to the nearest whole number we get 12 as the position of the 25th  percentile which is $385,000

Third quartile =(45+1)*75/100=46*0.75=34.5

Rounding off to the nearest whole number we get 35 as the position of the 75th percentile which is $670000

  1. Explaining the 70th percentile

This show that 70% of the “Sold price” for the sample are below $555,000

  1. Determining and explaining the interquartile range

Interquartile range=Quartile 3-Quartile 1

=$670000-$385000=$285000

This shows that the sold price spread between the 25th percentile and 75th percentile is $285000

Task 4

  1. Excel output for the descriptive statistics

Table 2: Descriptive statistics of sold Price

Sold Price ($000)

Mean

584.25

Standard Error

53.560

Median

453

Mode

400

Standard Deviation

359.289

Sample Variance

129,088.82

Kurtosis

3.986

Skewness

1.969

Range

1760

Minimum

125

Maximum

1885

Sum

26291.25

Count

45

 

  1. Upper and lower inner fence limit (IFUL and IFLL)

Q3=$670000, Q1=$385000, IQR=$285000 (From results in task 3)

IFUL = Q3 + 1.5 x IQR  

Replacing the values we get

IFUL=$670000+1.5($285000) =$ 665000+$427500

=$1097500     

IFLL = Q1 – 1.5 x IQR 

IFLL=$385000-1.5($285000) =$382000-$427500

=$-42500

  1. Choosing
    1. An appropriate measure of central tendency, and

Mean

  1. An appropriate measure of dispersion for your sample “Sold Price” data.

Stardard deviation

  • Provide a brief explanation of the reasoning behind your choice in both cases.

 Mean because it takes into consideration the value of the other observations

Standard deviation because it considers the dispersion from a measure of central tendency in this case the mean therefore considering all the observations

Task 5

Using descriptive statistics obtained in task four

  1. Identifying three pieces of evidence that indicate whether your sample “Sold Price” data has been obtained from a normally distributed population or not.
    1. Kurtosis

Shows whether the data is heavily tailed on not. A high value shows data with heavy tails or outliers while low values shows data with ligh tails or that lacks outliers

  1. Skewness

A positive or negative value shows that the data

  • Mean and standard deviation
  1. Applying the Standard Normal tables, calculate how many “Sold Price” observations in your sample would expect to lie within 1.5 standard deviations of the mean (i.e. between z = –1.5 and z = +1.5).
    1. Within +1.5 deviations has a probability of 0.9332 while within -1.5 deviations has a probability of 0.0668

The difference between +1.5 deviations and -1.5 deviations gives a probability of 0.8664

Thus 86.64% of the observations, that is, 45*0.8664=39 which is approximately 39 observations lie within ±1.5 deviations

  1. Using the mean and standard deviation, the 1.5 bound from the mean

Upper bound=Mean+1.5(Standard deviations)

Upper bound=$584250+1.5($359289) =$1123184

Lower bound=$584250-1.5($359289) =$45316.5

Number of observations between the bound = 39

The count exactly equals the calculated number of observations in (b)

This results confirms my results in a that most of the observations lie within 3 standard deviations (Cumming, 2013, 2014: Hoekstra et al., 2014)

 

 

 

Task 6

  1. Excel output for the descriptive statistics

Table 3: Descriptive statistics of sold Price

Sold Price ($000)

Mean

584.25

Standard Error

    53.560

Median

453

Mode

400

Standard Deviation

  359.289

Sample Variance

129088.8

Kurtosis

     3.986

Skewness

     1.969

Range

1760

Minimum

125

Maximum

1885

Sum

26291.25

Count

45

Confidence Level(90.0%)

    89.993

 

  1. A point estimate of the mean Sold Price” of the population of properties.

The point estimate is $584250

  1. A 90% confidence interval estimate of the mean “Sold Price” of the population of properties.

A 90% confidence interval

=Mean ±Zα(SE)

=$584250+$89993=$674243

=$584250-$89993=$494257

The 90% confidence interval is the $494257<”Sold Price”<$674243

  • Make a brief verbal statement explaining the meaning of the confidence interval

This means that the “sold price” mean will accurately lie within $494257 and $674243 at a 90% of the times the sample is taken from the population and its mean computed

  1. If the population mean “Sold Price” is actually 650 ($000s), would you consider the interval estimate obtained in (a), to be satisfactory?

I would consider the interval estimate satisfactory because it lies within the interval computed

Task 7

  1. Excel output

Table 4 : Descriptive statistics of building type

Building Type (0=nbv, 1=bv)

Mean

       0.46

Standard Error

       0.07

Median

          -  

Mode

          -  

Standard Deviation

       0.50

Sample Variance

       0.25

Kurtosis

-      2.06

Skewness

       0.17

Range

       1.00

Minimum

          -  

Maximum

       1.00

Sum

     23.00

Count

     50.00

Confidence Level(99.0%)

       0.19

  1. A point estimate of the proportion of brick veneer properties in the population.

The point estimate is 0.46 or 46%

  1. A 99% confidence interval estimate of the proportion of brick veneer properties in the population. (Mean=0.46, Confidence interval=0.19)

The 99% confidence interval is

=Proportion ±Zα(SE) or Proportion ±Confidence interval

=0.46+0.19=0.65

=0.46-0.19=0.27

Therefore the 99% confidence interval for the proportion of brick veneer properties in the population is 0.27<Proportion<0.65

  1. A 95% confidence interval estimate of the proportion of brick veneer properties in the sample using the formula (Mean=0.46, Standard error=0.07)

(sample statistic) ± (critical z or t) ´ (standard error of the sample statistic)

=0.46±1.960(0.07)=

=0.46+.1372=0.60

=0.46-0.1372=0.32

Therefore the 95% confidence interval for the proportion of brick veneer properties in the population is 0.32<Proportion<0.60

The direction of the change in precision is expected in the two because of the change in the level of confidence. A lower confidence interval lower the Z-statistic and thus reduces the interval range.

References

Cumming, G. (2014). The new statistics: Why and how. Psychological science, 25(1),7-29

Cumming, G. (2013). Understanding the new statistics: Effect sizes, confidence intervals and          meta-analysis. Routledge

Frankfort-Nachmias, C., & Leon-Guerrero, A. (2015). Social statistics for a diverse society (7th    ed.). Thousand Oaks, CA: Sage Publications. Chapter 3, “Graphic Presentation” (pp. 65–   95)

 Hoekstra, R., Morey, R. D., Rouder, J. N., & Wagenmakers, E. J. (2014). Robust misinterpretation          of confidence intervals. Psychonomic bulletin & review , 21 (5), 1157-1164

 

 

 

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