chapter 1: STATS – STATISTICS DATA AND STATISTICAL THINKING 1.1 The science of statistics
* Statistics - is the science of data. It involves collecting, classifying, summarising, organising, analysing, and interpreting numerical information. 1.2 types of statistical applications in business

* Descriptive Statistics - describe collected data. Utilizes numerical and graphical methods to look for patterns in data, summarize the information in the data and to present the information in a convenient form * Inferential Statistics – utilizing sample data to make estimates, predictions or other generalizations about a larger data set. * Estimation (Making conclusions of a population based on sample) * Hypothesis Testing (Testing on a belief)

* Confidence Interval (33 +/8) %
1.3 fundamental elements of statistics
* Experimental Unit – object (eg: person, thing, transaction, or event) of interest for which data is collected * Example – graduating NBS students in 2013
* Example – FOX viewers
* Example – Cola consumers
* Population – the set of units we are interested in learning about * Example – all 1200 graduating students at NBS in 2013 * Example – all FOX viewers
* Example – all Cola consumers
* Variable – characteristic or property of a single experimental unit * Any particular characteristic may vary among the experimental units in a population * Example – salary at first job after graduation

* Example – Age (in years) of each viewer
* Example – Cola preference
* Sample – subset of population
* Example – 100 graduating students who replied to online survey * Example – 200 FOX viewers selected by the executive * Example – 1,000 cola consumers selected from the population * Statistical Inference – generalization about a population based on sample data * Example – the average salary on graduation is...

...1. First enter the Real estate data (text book data set 1) into SPSS and then select a sample of size, n = last two digits of your ID and answer the exercises.
I. Select an appropriate class interval and organize the “Selling price” into a frequency distribution.
II. Compute the Mean, Median, Mode, Standard Deviation, Variance, Quartiles, 9th Decile, 10th Percentile and Range of “Selling price” from the raw data of your sample and interpret.
III. Develop a histogram (Using question “1”) for the variable “selling cost”.
IV. Develop a Pie chart and a Bar diagram for the variable “Township”.
V. Develop a Box plot for the variable “Distance”.
What information can you give from these plots?
Note: Comment on all your findings, charts and diagrams.
Answer to the question No.1
I. Selection of an appropriate Class interval
We select a sample size of n = 39 (last two digits of ID: 39)
To determine the number of Classes (k)
2 ≥ n
2≥ 64
So the recommended number of classes is 7.
Selling Price in TK. 000 (Thousand)
N
Valid
39
Missing
0
Minimum
1390.9
Maximum
3450.3
Class interval
So 343.233300
So the class intervals are,
1→1300 ─ 1600
2→1600─1900
3→1900─2200
4→2200─2500
5→2500─2800
6→2800─3100
7→3100─3400
8→3400-3700
From SPSS output, we get the following frequency distribution of selling price of homes sold in Denver, Colorado:
Frequency Distribution of Selling Price...

...of testing of two samples means were because it compared the two sets of data that are directly related to each other. The reason why I believed that rural homes have a lower average of beds due to the fact that rural areas are the countryside rather than the big known towns or towns of the state.
The population that my data set represents was the number of beds that the in-patients had in each of the homes between non-rural home and rural home facilities. The reason why the data was collected was because the Department of Health and Social Services of the State of New Mexico and cover 60 licensed nursing facilities in New Mexico in 1988. The methods that were used to collect the data was by the number of beds that were used in the home, annual medical in patient days (hundreds), annual total patient days (hundreds), annual total patient care revenue ($hundreds), annual nursing salaries ($hundreds), annual facilities expenditures ($hundred), and where the home was located between non-rural and rural areas. The source of the data set of the nursing home information toward New Mexico in 1988 was part of the data analyzed by Howard L. Smith, Niell F. Piland, and Nancy Fisher. This was published in the Journal of Rural Health in winter 1992. This data set can be calculated in four different types of forms. It can be calculated in a health, consumer, medical, and economics...

...
Simply use statistics as a tool. You will be given a data. (Next year you will not be given data, you will gather data yoruself).
1. Data: one of the variables is dependent and other dependent. Can be multiple. Then do regression analysis. ANOVA for overall significance and Regression equation. And write based on ANOVA there is a significance or not.
2. Some comments on correlation: volume vs. horse power etc.
3. Hypothesis test of one population. I assume that the mean is etc etc. Small paragraph analysis below the results of the test. ANOVA for small, large and medium size businesses for example.
Simply use statistics as a tool. You will be given a data. (Next year you will not be given data, you will gather data yoruself).
1. Data: one of the variables is dependent and other dependent. Can be multiple. Then do regression analysis. ANOVA for overall significance and Regression equation. And write based on ANOVA there is a significance or not.
2. Some comments on correlation: volume vs. horse power etc.
3. Hypothesis test of one population. I assume that the mean is etc etc. Small paragraph analysis below the results of the test. ANOVA for small, large and medium size businesses for example.
Simply use statistics as a tool. You will be given a data. (Next year you will not be...

...number of useful models and techniques that can be used in the
decision making process. Two important themes run through the study guide: data analysis and
decision making techniques.
Firstly we look at data analysis. This approach starts with data that are manipulated or processed
into information that is valuable to decision making. The processing and manipulation of raw
data into meaningful information are the heart ofdata analysis. Data analysis includes data
description, data inference, the search for relationships in data and dealing with uncertainty
which in turn includes measuring uncertainty and modelling uncertainty explicitly.
In addition to data analysis, other decision making techniques are discussed. These techniques
include decision analysis, project scheduling and network models.
Chapter 1 illustrates a number of ways to summarise the information in data sets, also known as
descriptive statistics. It includes graphical and tabular summaries, as well as summary measures
such as means, medians and standard deviations.
Uncertainty is a key aspect of most business problems. To deal with uncertainty, we need a basic
understanding of probability. Chapter 2 covers basic rules of probability and in Chapter 3 we
discuss the important concept of...

...Statistics
Refers to numerical facts, as an aggregate of figures or a collection of data.
Refers to a group of methods that are used to collect, analyze, present and interpret data and to arrive at conclusions or make decisions.
Descriptive Statistics
Deals with methods of recording/ tabulating data with their visual presentation, with the properties of various kinds of measures, with devices for computing them, and in fact, with all means of giving a summary description of the data themselves.
Ex.
A supervisor in charge of 40 clerks would like to know their average salary.
A sports writer wishes to list the highest goal makers in all basketball games in the last 5 NCAA seasons.
Inferential Statistics
Deals with inferences, conclusion, and/or forecast about an entire set of data that may be drawn from the analysis of a subset of this set of data.
Ex. A tire dealer wishes to estimate the average life of a particular brand of tire.
Ex. A company projects a 50% growth in the next five years after analyzing its revenue for the past five years.
Population
Consist of all elements – individuals, items or objects – whose characteristics are being studied. The number of elements in a population is called population size (n).
Sample
It is a portion of the population selected for study. The number of elements in a sample is called sample size...

...Random Sampling Method. In this case study, Mr Kwok collected a random sample of 1000 flights and proportions of three routes in the sample. He divides them into different sub-groups such as satisfaction, refreshments and departure time and then selects proportionally to highlight specific subgroup within the population. The reasons why Mr Kwok used this sampling method are that the cost per observation in the survey may be reduced and it also enables to increase the accuracy at a given cost.
TABLE 1: Data Summaries of Three Routes
Route 1
Route 2
Route 3
Normal(88.532,5.07943)
Normal(97.1033,5.04488)
Normal(107.15,5.15367)
Summary Statistics
Mean
88.532
Std Dev
5.0794269
Std Err Mean
0.2271589
Upper 95% Mean
88.978306
Lower 95% Mean
88.085694
N
500
Sum
44266
Summary Statistics
Mean
97.103333
Std Dev
5.0448811
Std Err Mean
0.2912663
Upper 95% Mean
97.676525
Lower 95% Mean
96.530142
N
300
Sum
29131
Summary Statistics
Mean
107.15
Std Dev
5.1536687
Std Err Mean
0.3644194
Upper 95% Mean
107.86862
Lower 95% Mean
106.43138
N
200
Sum
21430
From the table above, the total number of passengers for route 1 is 44,266, route 2 is 29,131 and route 3 is 21,430 and the total numbers of passengers for 3 routes are 94,827.
Although route 1 has the highest number of...

...Chapter 1
Statistics & Data
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Applications in Business and Economics
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Descriptive Statistics
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Inferential StatisticsStatisticsData overload!
I need help!
Slide 1
Applications in
Business and Economics
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Accounting
Public accounting firms use statistical
sampling procedures when conducting
audits for their clients.
Economics
Economists use statistical information
in making forecasts about the future of
the economy or some aspect of it.
Statistics
Slide 2
Applications in
Business and Economics
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Marketing
Electronic point-of-sale scanners at
retail checkout counters are used to
collect data for a variety of marketing
research applications.
Production
A variety of statistical quality
control charts are used to monitor
the output of a production process.
Statistics
Slide 3
Applications in
Business and Economics
Finance
Financial advisors use price-earnings ratios and
dividend yields to guide their investment
recommendations.
Statistics
Slide 4
Statistical Methods
St i i
at st cal
M et
hods
D escri i
pt ve
St i i
at st cs
Statistics
Inf
erent al
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St i i
at st cs
Slide 5
What Is Statistics?
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