This material is a part of PGPSE / CSE study material for the students of PGPSE / CSE students. PGPSE is a free online programme for all those who want to be social entrepreneurs / entrepreneurs
Introduction to Statistics - Basic Statistical Termssheisirenebkm
This is a presentation which focuses on the basic concepts of statistics. It includes the branches of statistics, population and sample, qualitative and quantitative data, and discrete and continuous variable.
Basics of Educational Statistics (Inferential statistics)HennaAnsari
Inferential Statistics
6.1 Introduction to Inferential Statistics
6.1.1 Areas of Inferential Statistics
6.2.2 Logic of Inferential Statistics
6.2 Importance of Inferential Statistics in Research
In Statistics Yates's correction for continuity (or Yates's chi-square test) is used in certain situations when testing for independence in a contingency table, let us understand it with illustration.
ANALYSIS ANDINTERPRETATION OF DATA Analysis and Interpr.docxcullenrjzsme
ANALYSIS AND
INTERPRETATION
OF DATA
Analysis and Interpretation of Data
https://my.visme.co/render/1454658672/www.erau.edu
Slide 1 Transcript
In a qualitative design, the information gathered and studied often is nominal or narrative in form. Finding trends, patterns, and relationships is discovered inductively and upon
reflection. Some describe this as an intuitive process. In Module 4, qualitative research designs were explained along with the process of how information gained shape the inquiry as it
progresses. For the most part, qualitative designs do not use numerical data, unless a mixed approach is adopted. So, in this module the focus is on how numerical data collected in either
a qualitative mixed design or a quantitative research design are evaluated. In quantitative studies, typically there is a hypothesis or particular research question. Measures used to assess
the value of the hypothesis involve numerical data, usually organized in sets and analyzed using various statistical approaches. Which statistical applications are appropriate for the data of
interest will be the focus for this module.
Data and Statistics
Match the data with an
appropriate statistic
Approaches based on data
characteristics
Collected for single or multiple
groups
Involve continuous or discrete
variables
Data are nominal, ordinal,
interval, or ratio
Normal or non-normal distribution
Statistics serve two
functions
Descriptive: Describe what
data look like
Inferential: Use samples
to estimate population
characteristics
Slide 3 Transcript
There are, of course, far too many statistical concepts to consider than time allows for us here. So, we will limit ourselves to just a few basic ones and a brief overview of the more
common applications in use. It is vitally important to select the proper statistical tool for analysis, otherwise, interpretation of the data is incomplete or inaccurate. Since different
statistics are suitable for different kinds of data, we can begin sorting out which approach to use by considering four characteristics:
1. Have data been collected for a single group or multiple groups
2. Do the data involve continuous or discrete variables
3. Are the data nominal, ordinal, interval, or ratio, and
4. Do the data represent a normal or non-normal distribution.
We will address each of these approaches in the slides that follow. Statistics can serve two main functions – one is to describe what the data look like, which is called descriptive statistics.
The other is known as inferential statistics which typically uses a small sample to estimate characteristics of the larger population. Let’s begin with descriptive statistics and the measures
of central tendency.
Descriptive Statistics and Central Measures
Descriptive statistics
organize and present data
Mode
The number occurring most
frequently; nominal data
Quickest or rough estimate
Most typical value
Measures of central
tendenc.
This material is a part of PGPSE / CSE study material for the students of PGPSE / CSE students. PGPSE is a free online programme for all those who want to be social entrepreneurs / entrepreneurs
Introduction to Statistics - Basic Statistical Termssheisirenebkm
This is a presentation which focuses on the basic concepts of statistics. It includes the branches of statistics, population and sample, qualitative and quantitative data, and discrete and continuous variable.
Basics of Educational Statistics (Inferential statistics)HennaAnsari
Inferential Statistics
6.1 Introduction to Inferential Statistics
6.1.1 Areas of Inferential Statistics
6.2.2 Logic of Inferential Statistics
6.2 Importance of Inferential Statistics in Research
In Statistics Yates's correction for continuity (or Yates's chi-square test) is used in certain situations when testing for independence in a contingency table, let us understand it with illustration.
ANALYSIS ANDINTERPRETATION OF DATA Analysis and Interpr.docxcullenrjzsme
ANALYSIS AND
INTERPRETATION
OF DATA
Analysis and Interpretation of Data
https://my.visme.co/render/1454658672/www.erau.edu
Slide 1 Transcript
In a qualitative design, the information gathered and studied often is nominal or narrative in form. Finding trends, patterns, and relationships is discovered inductively and upon
reflection. Some describe this as an intuitive process. In Module 4, qualitative research designs were explained along with the process of how information gained shape the inquiry as it
progresses. For the most part, qualitative designs do not use numerical data, unless a mixed approach is adopted. So, in this module the focus is on how numerical data collected in either
a qualitative mixed design or a quantitative research design are evaluated. In quantitative studies, typically there is a hypothesis or particular research question. Measures used to assess
the value of the hypothesis involve numerical data, usually organized in sets and analyzed using various statistical approaches. Which statistical applications are appropriate for the data of
interest will be the focus for this module.
Data and Statistics
Match the data with an
appropriate statistic
Approaches based on data
characteristics
Collected for single or multiple
groups
Involve continuous or discrete
variables
Data are nominal, ordinal,
interval, or ratio
Normal or non-normal distribution
Statistics serve two
functions
Descriptive: Describe what
data look like
Inferential: Use samples
to estimate population
characteristics
Slide 3 Transcript
There are, of course, far too many statistical concepts to consider than time allows for us here. So, we will limit ourselves to just a few basic ones and a brief overview of the more
common applications in use. It is vitally important to select the proper statistical tool for analysis, otherwise, interpretation of the data is incomplete or inaccurate. Since different
statistics are suitable for different kinds of data, we can begin sorting out which approach to use by considering four characteristics:
1. Have data been collected for a single group or multiple groups
2. Do the data involve continuous or discrete variables
3. Are the data nominal, ordinal, interval, or ratio, and
4. Do the data represent a normal or non-normal distribution.
We will address each of these approaches in the slides that follow. Statistics can serve two main functions – one is to describe what the data look like, which is called descriptive statistics.
The other is known as inferential statistics which typically uses a small sample to estimate characteristics of the larger population. Let’s begin with descriptive statistics and the measures
of central tendency.
Descriptive Statistics and Central Measures
Descriptive statistics
organize and present data
Mode
The number occurring most
frequently; nominal data
Quickest or rough estimate
Most typical value
Measures of central
tendenc.
initial postWhat are the characteristics, uses, advantages, and di.docxJeniceStuckeyoo
initial post
What are the characteristics, uses, advantages, and disadvantages of each of the measures of location and measures of dispersion? Discuss them with examples
first reply
Measures of location and measures of dispersion are two different ways of describing quantitative variables. Measures of location are often known as averages. Measures of dispersion are often known as a variation or spread. Both measures are helpful with describing statistical information. (Lind, Marchal, & Wathen, 2015)
The different measures of location include: the arithmetic mean, the median, the mode, the weighted mean, and the geometric mean. All of these measures of location pinpoint the center of a distribution of data. An advantage of measures of location is that the averages show us the central value of the data. A disadvantage of only using measures of location is that we may not draw an accurate conclusion because an average does not tell the spread of the data. Some examples of using measures of location include: finding the average price of a concert ticket, finding the average age of homeowners in a community, finding the averages shoe size of boys between the ages of 13-19, and finding the average amount of money people spend on food annually. (Lind, Marchal, & Wathen, 2015)
The different measures of dispersion include: the range, the variance, and the standard deviation. All of these measures of dispersion tell us about the spread of the data and it helps us compare the spread in two or more distributions. Advantages of using measures of dispersion are that it gives us a better idea of the range in which an average was calculated, and it is easy to calculate and understand. A disadvantage of using measures of dispersion is that it is a broad measurement because it only shows the maximum and minimum values of data. For example, the salaries of dentists in the state of Georgia might range from $70,000-$120,000 (just a made up example – not necessarily accurate data). This information is great for someone to know the range of dentist salaries, but it lacks in showing specific information about dentists’ salaries. (Lind, Marchal, & Wathen, 2015)
Lind, D. A., Marchal, W. G., & Wathen, S. A. (2015). Statistical techniques in business & economics. New York, NY: McGraw-Hill Education.
Second Reply
What are the characteristics, uses, advantages, and disadvantages of each of the measures of location and measures of dispersion? Discuss them with examples.
These are the measures in common use of location and dispersion: arithmetic mean, median, mode, weighted mean, and geometric mean. The arithmetic mean, median, and mode The mean usually refers to the arithmetic mean or average. This is just the sum of the measurements divided by the number of measurements. We make a notational distinction between the mean of a population and the mean of a sample. The general rule is that Greek letters are used for population characteristics and Latin letters ar.
Statistics What you Need to KnowIntroductionOften, when peop.docxdessiechisomjj4
Statistics: What you Need to Know
Introduction
Often, when people begin a statistics course, they worry about doing advanced mathematics or their math phobias kick in. Understanding that statistics as addressed in this course is not a math course at all is important. The only math you will do is addition, subtraction, multiplication, and division. In these days of computer capability, you generally don't even have to do that much, since Excel is set up to do basic statistics for you. The key elements for the student in this course is to understand the various types of statistics, what their requirements are, what they do, and how you can use and interpret the results. Referring back to the basic components of a valid research study, which statistic a researcher uses depends on several things:
The research question itself
The sample size
The type of data you have collected
The type of statistic called for by the design
All quantitative studies require a data set. Qualitative studies may use a data set or may use observations with no numerical data at all. For the purposes of the next modules, our focus will be on quantitative studies.
Types of Statistics
There are several types of statistics available to the researcher. Descriptive statistics provide a basic description of the data set. This includes the measures of central tendency: means, medians, and modes, and the measures of dispersion, including variances and standard deviations. Descriptive statistics also include the sample size, or "N", and the frequency with which each data point occurs in the data set.
Inferential statistics allow the researcher to make predictions, estimations, and generalizations about the data set, the sample, and the population from which the sample was drawn. They allow you to draw inferences, generalizations, and possibilities regarding the relationship between the independent variable and the dependent variable to indicate how those inferences answer the research question. Researchers can make predictions and estimations about how the results will fit the overall population. Statistics can also be described in terms of the types of data they can analyze. Non-parametric statistics can be used with nominal or ordinal data, while parametric statistics can be used with interval and ratio data types.
Types of Data
There are four types of data that a researcher may collect.
Nominal Data Sets
The Nominal data set includes simple classifications of data into categories which are all of equal weight and value. Examples of categories that are equal to each other include gender (male, female), state of birth (Arizona, Wyoming, etc.), membership in a group (yes, no). Each of these categories is equivalent to the other, without value judgments.
Ordinal Data Sets
Ordinal data sets also have data classified into categories, but these categories have some form or order or ranking attached, often of some sort of value / val.
This will help you to understand the basic statistics particularly Discriptive Statistics.
Basic terminologies used in statistics,measure of central tendancy,measure of frequency,measure of dispersion.
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3. Mean
Mean is a part of descriptive statistics. It is the average of the given data set. You can calculate the mean by adding
all the values of the data set and then divide the values’ sum by the number of values in the data set.
For example, if you have the data set of students age i.e., 16, 18, 17, 20, 15 years. In this case, you can calculate the
mean by adding all the values i.e., 86 years. And then you need to divide it from the total number of values i.e., 5.
Now the mean is 86/5= 17.2 years.
The median is the part of central tendency. Median can be found by arranging the observations in order from the
smallest to the most significant values. Median is the middle value of the data set. If the data set contains the odd
numbers of observations, then the middle value automatically becomes the median.
On the other hand, if we have an even number of observations, then the median is calculated by the average of the
middle values. For example, the data set of students age i.e., 16, 18, 17, 20, 15 years. In this data set, the median is 17
years.
Median
STATISTICS
BASIC TERMS
4. STATISTICS
BASIC TERMSMode
The mode is the value that appears most often in the given dataset. Mode value is more likely to be sampled from the
given data set. For example you have a data set of 10 student’s age i.e. 13, 13, 14, 14, 15,16, 16, 16, 17, 17. Here in this
given date, set 16 is the mode because it is occurring three times.
The significance in statistics is statistical hypothesis testing. It is less likely to occur and give the null hypothesis.
Significance
P-value
The P-value works as evidence against the null hypothesis. In other words, it is used to reject the null hypothesis. If
you have a smaller p-value, then the null hypothesis would have stronger evidence to reject the null hypothesis. More
often, the P-value expressed in the form of decimal numbers. But if you cover these values into the percentage. Then
you can easily understand that these values, i.e., 0.0452, are 4.52%.
5. Correlation
Correlation is one of the widely used statistical terms. In fact, it is the statistical technique Correlation is an
analytical technique that is used to show the relationship between the pairs. We can get to know how
strongly the pairs are related to one another with the help of correlation. For example, height and weight are
related to each other. For instance, taller people would have a heavyweight than short people.
The r-value In statistics measures the strength and direction of the linear relationship between two different
variables that are plotted on the scatterplot. The value of r is always between 1 and -1. You need to make
sure that your correlation r-value is close to 1 or -1. In this way, it becomes easy to interpret r values.
R-value
STATISTICS
BASIC TERMS
6. STATISTICS
KEY TERMSPopulation
The population is statistics is the set of similar items and events that may have a similar interest to some
questions and experiments. It can be a group of existing objects and a potentially infinite group of objects.
In statistics, the parameter is also known as the population parameter. It is the quantity of the population that
we enter into the probability distribution of statistics. Apart from that, we can also consider it as the numerical
characteristic of a statistical population. In other words, it uses quantitative characteristics of the population
that you are going to use for testing.
Parameter
Descriptive statistics
It is the descriptive coefficient that is used to summarize the given data set. You can represent the entire data
set or the sample to the data set. Descriptive statistics has two major parts i.e., the measure of central
tendency and measure of variability. The sample mean, median, mode, standard deviation, correlation, and
regression is the part of descriptive statistics.
7. STATISTICS
KEY TERMS
Statistical inference
It is the process that uses data analytics to deduce the properties of the underlying distributions of statistics.
We use it to conclude the given data set. There are four major types of statistics inference i.e., regression,
confidence intervals, and hypothesis tests.
The skew occurs when we have more scores toward one end of the distribution as compared with the other.
Apart from that, the negative skew occurred when we have the scores clustered at the high end, and the fewer
scored on the low end in a tail. On the other hand, if the distribution has a tail at the high end, you will have a
positive skew.
Skew
8. STATISTICS
KEY TERMS
Range
The range is widely used in statistics terms in research. It is the distance between the maximum as well as the
minimum values of the distribution.
Statistics variance is simply the statistical average of the dispersion of scores in the statistics distribution. It is
used with the standard deviation other than that it is not entirely useful in statistics.
Variance
Standard Deviation
The standard deviation is the measure of the variation amount and the depression of a set of values. If the
value trend is close to the set of the means, then the standard deviation would be low. On the other hand, if the
value spread out over the wider range, there would be a high standard deviation.
9. STATISTICS
KEY TERMS
Data
Data is the set of observations that can be collected from various mediums. The data is divided into two parts i.e., the
quantitative data and the qualitative data. Quantitative data can be measured easily because it has numeric values. It
is further divided into two groups, i.e., the discrete and continuous data.
The discrete data are those data values where we know the exact number i.e., the number of students in the class. And
the continuous data is where we don’t know the exact value of data i.e., the weight of the language. On the other hand,
the quantitative data is not present in the numerical values i.e., the hobbies of a group of individuals.
Probability is one of the major branches of mathematics. But it is the crucial term of statistics and widely used with
advanced statistics. It is used to measure how likely the given event is going to occur. Probability is measured between
the values 0 and 1. If the value is 0, then it is impossible for the event. And if the value is 1 then it is certain that the
event will happen. There are various types of probability and probability distributions, and it is widely used in data
science and big data analytics.
Probability
10. Conclusion
Let’s end this blog with these basic and critical statistics terms. We know that there
are more statistics terms which you can find in statistics glossary i.e., various types of
tests in statistics, ANOVA, MANOVA, theorems, and lots more. But here we have
mentioned those statistics terms that will help you a lot with your statistics education
as well as your profession.
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