ASSIGNMENT SUBMISSION FORM
Course Name:SMMD
Assignment Title:Assignment 1 (HousePrices.jmp)
Submitted by: Garima Agrawal (Section D)
(Student name or group name)
Group Member Name| PG ID|
Garima Agrawal| 61410506|

Question1:
The data for home values has a considerable wide range (429578) as compared to the inter-quartile range (93522). This means the data has a huge spread and the same can be verified from coefficient of variation which is even more than 41%. Besides, as can be seen from graphical plot and the positive skewness (0.87) measure, the data is skewed towards right. Also, the outliers present towards the right end indicate the presence of few extremely high valued houses, due to which average price of houses is higher than the median price. The highest density of data is present in two lower quartiles, as can be seen from box plot. This shows that low valued houses are present in bulk, and thus must available in the market easily. | | Question 2:

Though normal distribution model is not an absolutely apt for the data set of prices, the data can still be analyzed by assuming normality owing to the fact that data points hover around the diagonal line of normal Quantile plot. Some data points also cross the permissible range, but the density of data (high in the middle, and low at the ends ) allows for the usage of normal distribution model.The same can be verified from the measure of Kurtosis (0.7) which is well in permissible range for usage of normal distribution model.| |

Question 3:

MEAN = 164K;STANDARD DEVIATION = 68K
A.Z1 (@ x as 92.8K) = (92.8 – 164)/68 = -1.04Z2 (@ x as 255.5K) = (255.5 – 164)/68 = 1.34P(Z1 < Z < Z2) = 0.9099 – 0.1492 = 0.7607Percentage probability is 76.07, which seems to be more than the actual value, basis what can be seen via boxplot.| B.Z1 (@ x as 232K) = (232 – 164)/68 = 1P( Z < Z1) = 0.8413Percentage probability is 84.13, which is consistent with what can be seen via...

...
(BHRM)
BUSINESS STATISTICS (BBI 1224)
Name :
Student ID# :
Semester :
Academic Honesty Policy Statement
I, hereby attest that contents of this attachment are my own work. Referenced works, articles, art, programs, papers or parts thereof are acknowledged at the end of this paper. This includes data excerpted from CD-ROMs, the Internet, other private networks, and other people’s disk of the computer system.
Student’s Signature :
SUPERVISOR’S COMMMENTS/GRADE:
for office use only
DATE : ------------------------
TIME : ________________
RECEIVER’S NAME : _______
ANSWER
1.
A) Amount of time spent shopping in the bookstore
Answer: Numerical variable-continuous-ratio scale.
B) Number of textbooks purchased
Answer: Numerical variable-discrete-ratio scale.
C) Gender
Answer: Categorical variable-nominal scale.
2.
The following is a set of data from a sample of
7 4 9 8 2
A) Compute the first quartile the third quartile and the interquartile range.
Rank: 2 4 7 8 9
First quartile = 2+4 / 2 = 3
Third quartile = 8+9 / 2 = 8.5
Interquartile range = third quartile – first quartile = 8.5 – 3 = 5.5
B) List the five-number summary.
1) X smallest
2) Q1
3) Median
4) Q3...

...Descriptive Statistics paper
RES/341
July 24, 2011
Descriptive Statistics paper
The information below is a continuance of week two, week three, and on week four. The previous assessment in week two on “real estate research” for thinking of hypothesis on home values in Alvarado, Texas. The evaluating on real estate prices reveals a purpose of this research paper and its importance findings. The discoveries include problem definition, and on variables.
The next assessment was on week three on “data collection” on reviewing literatures, sampling design, and on any ethical concerns with collection data on the same topic. A summary was assembled in week three on terms of population, sampling size, and factors on real estate. This research found house prices to change in each different region.
This is week four paper on “descriptive statistics” on real estate in Alvarado, Texas. The information below will consist of; data analysis, data using graphic and tabular techniques, and on skew values, histogram measures, and on central tendency.
The Central tendency is the measures of numerical summaries used to summarize data with a one number. The most common used are mode, mean, and median. The Hypothesis is "homes more or less expensive fifteen miles away from the center of the city"? The comparison will come from the City of Arlington, Texas, and Cedar Hill. The mode is the data that happens most frequently in the data...

...Stem-and-leaf displays and dot plots are useful for detecting
Student Answer:
Outliers
Skewness
Midpoint of data
All of the above
Points Received: 1 of 1
Comments:
4. Question : When a population is skewed to the left or right with a very long tail, what is the best measure to use for central tendency.
Student Answer: Population mean
Population mode
Population median
Population standard deviation
Points Received: 1 of 1
Comments:
5. Question : The science of using a sample to make generalizations about the important aspects of a population is known as
Student Answer: Statistical Process Control.
Descriptive Statistics.
A random sample.
Statistical Inference.
Points Received: 1 of 1
Comments:
6. Question : The standardized value of any value in a population or sample is called
Student Answer: percentile
coefficient of variation
quartile
Z-score
Points Received: 1 of 1
Comments:
7. Question : It is appropriate to use the Empirical Rule to describe a population that is extremely skewed.
Student Answer: True
False
Points Received: 1 of 1
Comments:
8. Question : The population mean is the average of the population measurements.
Student Answer:
True
False...

...Basics of Statistics
Jarkko Isotalo
30
20
10
Std. Dev = 486.32
Mean = 3553.8
N = 120.00
0
2400.0
2800.0
2600.0
3200.0
3000.0
3600.0
3400.0
4000.0
3800.0
4400.0
4200.0
4800.0
4600.0
5000.0
Birthweights of children during years 1965-69
Time to Accelerate from 0 to 60 mph (sec)
30
20
10
0
0
Horsepower
100
200
300
1
Preface
These lecture notes have been used at Basics ofStatistics course held in University of Tampere, Finland. These notes are heavily based on the following
books.
Agresti, A. & Finlay, B., Statistical Methods for the Social Sciences, 3th Edition. Prentice Hall, 1997.
Anderson, T. W. & Sclove, S. L., Introductory Statistical Analysis. Houghton Miﬄin Company, 1974.
Clarke, G.M. & Cooke, D., A Basic course in Statistics. Arnold,
1998.
Electronic Statistics Textbook,
http://www.statsoftinc.com/textbook/stathome.html.
Freund, J.E.,Modern elementary statistics. Prentice-Hall, 2001.
Johnson, R.A. & Bhattacharyya, G.K., Statistics: Principles and
Methods, 2nd Edition. Wiley, 1992.
Leppälä, R., Ohjeita tilastollisen tutkimuksen toteuttamiseksi SPSS
for Windows -ohjelmiston avulla, Tampereen yliopisto, Matematiikan, tilastotieteen ja ﬁlosoﬁan laitos, B53, 2000.
Moore, D., The Basic Practice of Statistics. Freeman, 1997.
Moore, D. & McCabe G., Introduction to the...

...
Descriptive Statistics
QNT/561
September 5, 2014
Descriptive Statistics Interpretation
Measuring productivity is paramount for the successful organization; in terms of profitability and progressively growing the business. The data was significantly skewed. Fifty five subjects were randomly selected. Their ages were between 19 and 55 years, with a mean of 37.84, mean of 41 and standard deviation of 9.11. The nurses work shifts varied from morning, mid shift, and overnight. Meade Medical Center strongly believes a 95% population productivity is affected by nurses age and work shift.
Descriptive statistics
Wage
count
55
mean
33,320.55
sample standard deviation
7,753.39
sample variance
60,115,073.77
minimum
19435
maximum
44975
range
25540
confidence interval 95.% lower
31,271.47
confidence interval 95.% upper
35,369.62
margin of error
2,049.08
z
1.96
1st quartile
27,980.00
median
33,425.00
3rd quartile
40,387.50
interquartile range
12,407.50
mode
19,435.00
low extremes
0
low outliers
0
high outliers
0
high extremes
0
Descriptive statistics
Age
count
55
mean
37.84
sample standard deviation
9.11
sample variance
82.99
minimum
19
maximum
55
range
36
confidence interval 95.% lower
35.43
confidence interval...

...correlation exists between the time period the workers have worked in the industry and their health effects.
Analysis will be carried out with the help of the following 5 samples:
* Worker ID
* Age
* Department
* Length of service
* Percentage of cell damage
The above samples are independent within and also between each other. To obtain an accurate analysis of the data, the normality, box plot and straight-line relationship and independence of the statistical analysis will be checked. The Null or Alternative Hypothesis will be accepted or rejected on the basis of a statistical analysis, which will be used to analyse the median percentage of damaged cells got from the brick and tile operations.
Table 1: Descriptive Statistics of brick and tile operation workers percentage damaged cells
Variable | N | N* | Mean | SE Mean | St: Dev. | Minimum | Q1 | Median | Q3 | Maximum |
% Damaged cells of Tile operation | 27 | 0 | 1.337 | 0.210 | 1.090 | 0.200 | 0.600 | 1.100 | 1.500 | 4.700 |
% Damaged cells of Brick operation | 38 | 0 | 1.532 | 0.179 | 1.106 | 0.200 | 0.536 | 1.370 | 2.189 | 4.562 |
Table 1 gives a descriptive data of the workers of the respective industries.As seen in the table above the % of damaged cells of the workers in the brick industry is higher when compared with the tile operation workers.The median percentage of brick industry workers is 1.370 which is higher as...

...Managerial Statistics
Distinguish between primary data and secondary data?
OBJECTIVE
The main objective of this topic is to measure the degree of relationship between the variables under consideration.The correlation analysis refers to the techniques used in measuring the closeness of the relationship between the variables.
DEFINITION
Some important definitions of correlation are given below:
1. “Correlation analysis deals with the association between two or more variables”. ---- Simpson & kafka.
2. “When the relationship is of quantitative nature, the appropriate statistical tool for discovering and measuring the relationship and expressing it in brief formula is known as correlation”.----- Croxton & Cowden.
3.Correlation analysis attempts to determine the “degree of relationship between variables”.----- Ya Lun Chou.
Thus correlation is a statistical device which helps us in analyzing the covariation of two or more variables.
TYPES OF CORRELATION
Correlation is described or classified in several different ways.Three of the most important ways of classifying correlation are:
1.Positive or negative 2.Simple, partial and multiple 3. Linear and non-linear
The various methods of studying correlation are
1.Scatter Diagram Method
2.Karl Pearson’s Coefficient of correlation.
3.Method of Least Squares [Of these , the first two methods shall be discussed as follows. ]
SCATTER DIAGRAM
What it is: A scatter diagram is a tool for analyzing...

...Descriptive and Inferential Statistics Paper
PSY 315
Descriptive and Inferential Statistics
Whether doing original research or conducting literature reviews, one must conclude what a powerful and versatile tool statistics are in the hands of researchers. From basic statistics such as data description, to using complex statistical methods to foresee future patterns or strengthen scientific claims about current climates, the role of statistics in research cannot be taken lightly and is essential in almost any field, especially in psychology. The statistical method is divided into two main branches called descriptive and inferential statistics. Descriptive statistics is a summary of information and the data presented is easily understood. Inferential statistics are much more detailed and are used to draw conclusions about hypotheses or determine probabilities of an outcome. Both allow researchers to describe, graph and present data for a general audience or more technical for the professionals. Without statistics, researchers lose that vital tool that allows them to move from hypothesis to conclusion.
The ability to describe data is an essential asset that comes with statistics. Once strong, reliable, and valid data is collected by the researcher, he or she must then make sense of the data and more importantly, make it...