1. What are descriptive statistics and how do they differ from inferential statistics?

INTRODUCTION
Statistical procedures can be divided into two major categories: descriptive statistics and inferential statistics. Typically, in most research conducted on groups of people, you will use both descriptive and inferential statistics to analyse your results and draw conclusions. So what are descriptive and inferential statistics? And what are their differences?We have seen that descriptive statistics provide information about our immediate group of data. For example, we could calculate the mean and standard deviation of the exam marks for the 100 students and this could provide valuable information about this group of 100 students. Any group of data like this, which includes all the data you are interested in, is called a population. A population can be small or large, as long as it includes all the data you are interested in. For example, if you were only interested in the exam marks of 100 students, the 100 students would represent your population. Descriptive statistics are applied to populations, and the properties of populations, like the mean or standard deviation, are called parameters as they represent the whole population (i.e., everybody you are interested in).Often, however, you do not have access to the whole population you are interested in investigating, but only a limited number of data instead. For example, you might be interested in the exam marks of all students in the UK. It is not feasible to measure all exam marks of all students in the whole of the UK so you have to measure a smaller sample of students (e.g., 100 students), which are used to represent the larger population of all UK students. Properties of samples, such as the mean or standard deviation, are not called parameters, but statistics. Inferential statistics are techniques that allow us to use these samples to make generalizations about the populations from which the samples were...

...Descriptive and InferentialStatistics
Greg Hagen, Jean Parker, Jade Riley, Nanika Woods, Myron Hamilton, Tajah Gutierrez, Treva Eley
PSY/315
June 9, 2012
Nancy A. Walker
Descriptive and InferentialStatisticsStatistics is a vital part of the psychological field as the analysis of statistics provides researchers with a means of both describing the results of research and surmising from those results, as well as drawing conclusions related to a hypothesis. Statistics gives a better understanding and exact description of a phenomenon of nature when in the field of study and includes data collection, data analysis, interpretation of, and making inferences from the data.This analysis is usually done on information that has been assembled in the form of graphs and charts of various types. Essentially, there are two branches of methodology involving statistics, descriptivestatistics and inferentialstatistics, with descriptivestatistics describing the research, and inferentialstatistics making inferences from it. Without statistics, researchers would have little foundation for the validation of their theories.
“Statistics is a form of...

...Professor Dumonceaux
DescriptiveStatistics Paper
2 June 2014
Finding a New Home
According to Trochim, “Descriptivestatistics are used to describe the basic features of the data in a study. They provide simple summaries about the sample and the measures. Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data” (Trochim, 2006). For many years, many studies and researches have been done in real estate market. Buyers need to conduct researches to decide which house they will purchase. Buyers’ concerns include the price of the house, the number of bedrooms, and location. Real estate agents need to gather all the necessary information to provide their services to buyers. Additionally, the agents must be able to predict what types of houses are most likely to sell. In this paper, I will provide the summary of what I have been studying. The paper will include the measure of central tendency, dispersion, and skew for data. In addition, this paper will also contain graphic data as well as tabular data to demonstrate my findings and studies. In the end, conclusion will present whether my research findings answered the problem statement or if more research may be needed.
Examining the data collected for the current real estate market desires, following are the conclusions based on its findings. There are many key factors to consider when...

...COM 208 InferentialStatistics (Panel 10)
With inferentialstatistics, you are trying to reach conclusions that extend beyond the immediate data alone. For instance, we use inferentialstatistics to try to infer from the sample data what the population might think. Or, we use inferentialstatistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance in this study. Thus, we use inferentialstatistics to make inferences from our data to more general conditions; we use descriptivestatistics simply to describe what's going on in our data.
Here, I concentrate on inferentialstatistics that are useful in experimental and quasi-experimental research design or in program outcome evaluation. Perhaps one of the simplest inferential test is used when you want to compare the average performance of two groups on a single measure to see if there is a difference. You might want to know whether eighth-grade boys and girls differ in math test scores or whether a program group differs on the outcome measure from a control group. Whenever you wish to compare the average performance between two groups you should...

...Modules WHAT IT IS Return to Table of Contents
Data Analysis: Analyzing Data - InferentialStatisticsInferentialstatistics deal with drawing conclusions and, in some cases, making predictions about the properties of a population based on information obtained from a sample. While descriptivestatistics provide information about the central tendency, dispersion, skew, and kurtosis of data, inferentialstatistics allow making broader statements about the relationships between data. Inferentialstatistics are frequently used to answer cause-and-effect questions and make predictions. They are also used to investigate differences between and among groups. However, one must understand that inferentialstatistics by themselves do not prove causality. Such proof is always a function of a given theory, and it is vital that such theory be clearly stated prior to using inferentialstatistics. Otherwise, their use is little more than a fishing expedition. For example, suppose that statistical methods suggest that on average, men are paid significantly more than women for full-time work. Several competing explanations may exist for this discrepancy. Inferentialstatistics can provide evidence to prove one theory more...

...Department of Decision Sciences
Rational Decision Making
Only study guide for
DSC2602
University of South Africa
Pretoria
c 2010 University of South Africa
All rights reserved.
Printed and published by the
University of South Africa,
Muckleneuk, Pretoria.
DSC2602/1/2011
Cover: Eastern Transvaal, Lowveld (1928) J. H. Pierneef
J. H. Pierneef is one of South Africa’s best known artists.
Permission for the use of this work was kindly granted
by the Schweickerdt family.
The tree structure is a recurring theme in various branches
of the decision sciences.
Preface
Everyday life is full of decisions. What should I wear today? What should I eat? Should I buy
the red or blue shirt? Should I buy a speciﬁc house or buy a piece of land? What is the shortest
route from my house to work? . . . And many more.
Some of these decisions can be made without thinking or by guesswork. Some can be solved by
reasoning or emotions. Some are a bit more diﬃcult and may need additional information.
People have been using mathematical tools to aid decision making for decades. During World
War II many techniques were developed to assists the military in decision making. These developments were so successful that after World War II many companies used similar techniques in
managerial decision making and planning.
The decision making task of modern management is more demanding and more important...

...Contents
Question 1 3
Question 2a 5
Question 2b 6
Question 2c 7
Question 3a 8
Question 3b 8
Question 3c 10
Question 3d 11
Question 4 12
Question 5 14
References 15
Question 1
The sampling method that Mr. Kwok is using is Stratified 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%...

...Statistics1
Business Statistics
LaSaundra H. – Lancaster
BUS 308 Statistics for Managers
Instructor Nicole Rodieck
3/2/2014
Statistics 2
When we hear about business statistics, when think about the decisions that a manager makes to help make his/her business successful. But do we really know what it takes to run a business on a statistical level? While some may think that business statistics is too much work because it entails a detailed decision making process that includes calculations, I feel that without educating yourself on the processes first you wouldn’t know how to imply statistics. This is a tool managers will need in order to run a successful business. In this paper I will review types of statistical elements like: Descriptive, Inferential, hypothesis development and testing and the evaluation of the results. Also I will discuss what I have learned from business statistics.
My description of Descriptivestatistics is that they are the numerical elements that make up a data that can refer to an amount of a categorized description of an item such as the percentage that asks the question, “How many or how much does it take to “ and the outcome...

...Organization of Terms
Experimental Design
DescriptiveInferential
Population
Parameter
Sample
Random
Bias
Statistic
Types of
Variables
Graphs
Measurement scales
Nominal
Ordinal
Interval
Ratio
Qualitative
Quantitative
Independent
Dependent
Bar Graph
Histogram
Box plot
Scatterplot
Measures of
Center
Spread
Shape
Mean
Median
Mode
Range
Variance
Standard deviation
Skewness
Kurtosis
Tests of
Association
Inference
Correlation
Regression
Slope
y-intercept
Central Limit Theorem
Chi-Square
t-test
Independent samples
Correlated samples
Analysis-of-Variance
Glossary of Terms
Statistics - a set of concepts, rules, and procedures that help us to:
organize numerical information in the form of tables, graphs, and charts;
understand statistical techniques underlying decisions that affect our lives and well-being; and
make informed decisions.
Data - facts, observations, and information that come from investigations.
Measurement data sometimes called quantitative data -- the result of using some instrument to measure something (e.g., test score, weight);
Categorical data also referred to as frequency or qualitative data. Things are grouped according to some common property(ies) and the number of members of the group are recorded (e.g., males/females, vehicle type).
Variable - property of an object or event that can take on...

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