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This document summarizes key concepts from the first three sections of Chapter 1 of an introductory statistics textbook. It defines important statistical terms like population, sample, parameter, and statistic. It also distinguishes between descriptive and inferential statistics, and different types of data like categorical, quantitative, discrete, and continuous data. Measurement levels like nominal, ordinal, interval, and ratio are explained. Examples are provided to illustrate the concepts.
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Homework #1
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Spring 20
Read the Syllabus and FAQ on how to do your homework before beginning the assignment!
To get consideration for full credit, you must:
· Follow directions;
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Id
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1
Did not grad HS
Yes
Low
2
HS dip
Yes
Low
3
HS dip
No
Med
4
BA
No
Low
5
BA
Yes
Med
6
MA
Yes
High
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HS dip
Yes
Med
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3
3200
Yes
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2
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2
1500
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ii. What is the level-of-measurement? Before answering, be sure to consult the slide called “Level of measurement – language to use”. Use the formal language!
iii. What are the values for the variable?
iv. If you had to make a guess, what do you think the “question” was that was asked of the unit-of-analysis to get these data? (for example, if we had a continuous variable called “num_pets” the question might be “How many pets live in your household?”)
3. For each of the following questions (1) construct a dataset with one variable and three observations (2) add data that could have theoretically been collected (just make up the actual responses to the question); and (3) indicate the level-of-measurement of the variable. I’ve done two examples for you.
Example#1:
What is your current age? (individual is the unit-of-analysis)
idage
1 25
2 32
3 61
The age variable is continuous/interval ratio.
Example#2:
What is the size of this hospital based on number of beds? (hospital is the unit-of-analysis)? Answers can be small (1-100 beds), medium (101-500 beds), large (501 beds to 1000 beds), extra large (1001+ beds)
idhosp_size
1 med
2 med
3 ext ...
Week 7 - sampling
The document discusses key concepts related to sampling and statistical inference in political science research. It defines populations and samples, and explains that samples are used to make inferences about populations that are too large to study in their entirety. It covers different sampling methods like simple random sampling, stratified sampling, and cluster sampling. It also discusses concepts like expected values, standard errors, sampling distributions, and how confidence intervals are used to assess the accuracy of sample estimates.
sophmoreyearmath-197-240.docx
Kelly grew pumpkins and tested the effect of an organic fertilizer by planting seeds in two patches, one with fertilizer and one without. She counted the number of pumpkins harvested from random plants in each patch. The results showed the number of pumpkins per plant was higher on average when fertilizer was used. To analyze the data, Kelly could organize it into a tally table, display it in a column graph, and describe features such as the mode and any outliers to determine the typical yield and spread between the two data sets. This would help her conclude if the fertilizer significantly increased pumpkin yields.
Week 7 Sampling
The document discusses key concepts related to sampling and statistical inference in political science research. It defines populations and samples, and explains that samples are used to make inferences about populations that are too large to study in their entirety. It covers different sampling methods like simple random sampling, stratified sampling, and cluster sampling. It also discusses concepts like expected values, standard errors, sampling distributions, and how confidence intervals are used to assess the accuracy of sample estimates.
Week 7 Sampling
The document discusses key concepts related to sampling and statistical inference in political science research. It defines populations and samples, and explains that samples are used to make inferences about populations that are too large to study in their entirety. It covers different sampling methods like simple random sampling, stratified sampling, and cluster sampling. It also discusses concepts like expected values, standard errors, sampling distributions, and how confidence intervals are used to assess the accuracy of sample estimates.
POL SOC 360 Sampling Generalizability
This document discusses key concepts related to sampling and generalizability in research methods. It defines a population as the entire set of individuals or entities to which study findings can be generalized. Sampling involves selecting a subset of units from the population to study. Probability samples allow inferences about the population, while nonprobability samples do not. Types of probability samples discussed include simple random samples, systematic samples, and stratified samples.
nossi ch 9
This document discusses different sampling methods used in collecting and interpreting data from populations. It defines key terms like population, sample, variable, and bias. Common sampling methods described include simple random sampling, systematic sampling, stratified sampling, cluster sampling, and quota sampling. Examples are provided to illustrate each method. The goal is to select samples that accurately represent the population being studied in an efficient manner.
Basics stat ppt-types of data
This presentation is about Basic Statistics-related to types of Data-Qualitative and Quantitative, and its Examples in everyday life- By: Dr. Farhana Shaheen
Definition statistics -unknown
Statistics involves collecting, organizing, analyzing, and interpreting numerical data. Descriptive statistics describes data while inferential statistics draws conclusions from samples to populations. A study on coyotes concluded they were all dangerous based on ranchers' observations of those near ranches, but further study found most coyotes avoid sheep. Experiments aim to observe changes from treatments by comparing experimental and control groups, while observational studies make uncontrolled observations. Surveys can introduce biases from question wording, non-responses, and over-generalizing results.
StatIstics module 1
This document provides an overview of key concepts in statistics, including:
- Statistics involves collecting, organizing, analyzing, and interpreting numerical data.
- Descriptive statistics describes data while inferential statistics draws conclusions about populations from samples.
- Variables can be quantitative (having numerical values) or qualitative (categorical).
- Random sampling is important for drawing valid statistical inferences from a sample to a population.
- Experiments involve imposing treatments to observe changes in variables, while observational studies do not alter variables.
- Survey questions require careful design to avoid biases and obtain meaningful results.
« PreviousHomeNext »Home » Measurement » Levels of Measure.docx
« PreviousHomeNext »
Home » Measurement » Levels of Measurement
The level of measurement refers to the relationship among the values that are assigned
to the attributes for a variable. What does that mean? Begin with the idea of the
variable, in this example "party affiliation." That variable has a number of attributes. Let's
assume that in this particular election context the only relevant attributes are
"republican", "democrat", and "independent". For purposes of
analyzing the results of this variable, we arbitrarily assign the values 1, 2 and 3 to the
three attributes. The level of measurement describes the relationship among
these three values. In this case, we simply are using the numbers as shorter placeholders
for the lengthier text terms. We don't assume that higher values mean "more" of
something and lower numbers signify "less". We don't assume the the value of 2
means that democrats are twice something that republicans are. We don't assume that
republicans are in first place or have the highest priority just because they have the
value of 1. In this case, we only use the values as a shorter name for the attribute.
Here, we would describe the level of measurement as "nominal".
Why is Level of Measurement Important?
First, knowing the level of measurement helps you decide how to interpret the data from
that variable. When you know that a measure is nominal (like the one just described), then
you know that the numerical values are just short codes for the longer names. Second,
knowing the level of measurement helps you decide what statistical analysis is appropriate
on the values that were assigned. If a measure is nominal, then you know that you would
never average the data values or do a t-test on the data.
There are typically four levels of measurement that are defined: Nominal Ordinal Interval Ratio
In nominal measurement the numerical values just "name" the attribute
uniquely. No ordering of the cases is implied. For example, jersey numbers in basketball
are measures at the nominal level. A player with number 30 is not more of anything than a
player with number 15, and is certainly not twice whatever number 15 is.
In ordinal measurement the attributes can be rank-ordered. Here, distances
between attributes do not have any meaning. For example, on a survey you might code
Educational Attainment as 0=less than high school; 1=some high school.; 2=high school degree; 3=some college;
4=college degree; 5=post college. In this measure, higher numbers mean more education.
But is distance from 0 to 1 same as 3 to 4? Of course not. The interval between values is
not interpretable in an ordinal measure.
In interval measurement
the distance between attributes does have meaning. For example, when we measure
temperature (in Fahrenheit), the distance from 30-40 is same as distance from 70-80. The
interval between values is interpretable. Because of this, it makes sense to compute an
average of an interval variable, where it doesn't make sens ...
Introduction to Statistics
This document provides an introduction to statistics. It discusses why statistics is important and required for many programs. Reasons include the prevalence of numerical data in daily life, the use of statistical techniques to make decisions that affect people, and the need to understand how data is used to make informed decisions. The document also defines key statistical concepts such as population, parameter, sample, statistic, descriptive statistics, inferential statistics, variables, and different types of variables.
Lect 1 Introduction Statistics.pdf
Statistics can be misused in several ways:
1) By using statistics to sell products or get attention through evoking fear or shock with selective or biased statistics.
2) By detaching statistics from their proper context or comparisons to make claims that are misleading.
3) By suggesting causal relationships through statistics that do not account for all variables or factors. Care must be taken to properly collect and interpret statistical data to avoid misleading conclusions.
1.1 statistical and critical thinking
This document provides an overview and objectives of Chapter 1: Introduction to Statistics from an elementary statistics textbook. It covers key statistical concepts like data, population, sample, variables, and the two branches of statistics - descriptive and inferential. Potential pitfalls in statistical analysis like misleading conclusions, biased samples, and nonresponse are also discussed. Examples are provided to illustrate concepts like voluntary response samples, statistical versus practical significance, and interpreting correlation.
Mazda Presentation Topic
The document discusses reasons to study statistics and the data analysis process. It provides three reasons: being informed, making informed judgments, and evaluating decisions that affect your life. It describes the nature of variability in data and defines key statistical concepts like population, sample, descriptive statistics, and inferential statistics. The data analysis process involves understanding the problem, collecting and summarizing data, applying statistical methods, and interpreting results. Examples are provided to illustrate key concepts.
Statistical Fundamentals in Total Quality Management
Statistical Fundamentals, The Five Basic Words of Statistics, Branches of Statistics, Sources of Data, Sampling Concepts, Sample Selection Methods
Chap01
This chapter introduces statistics concepts for business and economics. It explains that decisions are often made with incomplete information due to uncertainty. Key terms are defined, including population, sample, parameter, and statistic. Random sampling is described as a procedure where each member of a population has an equal chance of being selected. The two main branches of statistics - descriptive and inferential - are examined. Descriptive statistics involves collecting and summarizing data, while inferential statistics allows for predictions, estimates, and hypothesis testing based on sample results. The decision making process is reviewed as moving from identifying a problem to collecting data, transforming it into information and knowledge, and making a decision.
Mth 540 Massive Success / snaptutorial.com
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Homework #1SOCY 3115Spring 20Read the Syllabus and FAQ on ho.docx
Homework #1SOCY 3115Spring 20Read the Syllabus and FAQ on ho.docx
« PreviousHomeNext »Home » Measurement » Levels of Measure.docx
« PreviousHomeNext »Home » Measurement » Levels of Measure.docx
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1.1 to 1.3
- 1. Chapter 1 Sections 1.1 to 1.3 Introduction to Statistics *Always read the chapter!
- 8. 1.3 Levels of Measurement of Data Level Summary Example Nominal Categories only. Data cannot be arranged into an ordering scheme. The states where students are born: 5 CA 20 TX 40 NY Categories or names only! Ordinal Categories are ordered, but differences can’t be found or are meaningless. The types of cars owned by students: 5 Compact 20 Mid-size 40 Full-Size An order is determined by “compact, mid-size, full-size.” Interval Differences are meaningful, but there is no natural zero starting point and ratios are meaningless. Campus temperatures: 5° F 20° F 40° F 0° F doesn’t mean “no heat.” 40° F is not twice as hot as 20° F. Ratio There is a natural zero starting point and ratios are meaningful. Student commuting distances: 5 mi 20 mi 40 mi 40 mi is twice as far as 20 miles.