1. Illustrate point and interval estimations.
2. Distinguish between point and interval estimation.
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Hypothesis is usually considered as the principal instrument in research and quality control. Its main function is to suggest new experiments and observations. In fact, many experiments are carried out with the deliberate object of testing hypothesis. Decision makers often face situations wherein they are interested in testing hypothesis on the basis of available information and then take decisions on the basis of such testing. In Six –Sigma methodology, hypothesis testing is a tool of substance and used in analysis phase of the six sigma project so that improvement can be done in right direction
1. Illustrate point and interval estimations.
2. Distinguish between point and interval estimation.
Visit the website for more services it can offer:
https://cristinamontenegro92.wixsite.com/onevs
Hypothesis is usually considered as the principal instrument in research and quality control. Its main function is to suggest new experiments and observations. In fact, many experiments are carried out with the deliberate object of testing hypothesis. Decision makers often face situations wherein they are interested in testing hypothesis on the basis of available information and then take decisions on the basis of such testing. In Six –Sigma methodology, hypothesis testing is a tool of substance and used in analysis phase of the six sigma project so that improvement can be done in right direction
INFERENTIAL STATISTICS: AN INTRODUCTIONJohn Labrador
For instance, we use inferential statistics to try to infer from the sample data what the population might think. Or, we use inferential statistics 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.
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Correlation
PSYCH/610 Version 2
1
University of Phoenix Material
Correlation
A researcher is interested in investigating the relationship between viewing time (in seconds) and ratings of aesthetic appreciation. Participants are asked to view a painting for as long as they like. Time (in seconds) is measured. After the viewing time, the researcher asks the participants to provide a ‘preference rating’ for the painting on a scale ranging from 1-10. Create a scatter plot depicting the following data:
Viewing Time in Seconds
Preference Rating
10
3
12
4
24
7
5
3
16
6
3
4
11
4
5
2
21
8
23
9
9
5
3
3
17
5
14
6
What does the scatter plot suggest about the relationship between viewing time and aesthetic preference? Is it accurate to state that longer viewing times are the result of greater preference for paintings? Explain. Submit your scatter plot and your answers to the questions to your instructor.
LEARNING OBJECTIVES
· Explain how researchers use inferential statistics to evaluate sample data.
· Distinguish between the null hypothesis and the research hypothesis.
· Discuss probability in statistical inference, including the meaning of statistical significance.
· Describe the t test and explain the difference between one-tailed and two-tailed tests.
· Describe the F test, including systematic variance and error variance.
· Describe what a confidence interval tells you about your data.
· Distinguish between Type I and Type II errors.
· Discuss the factors that influence the probability of a Type II error.
· Discuss the reasons a researcher may obtain nonsignificant results.
· Define power of a statistical test.
· Describe the criteria for selecting an appropriate statistical test.
Page 267IN THE PREVIOUS CHAPTER, WE EXAMINED WAYS OF DESCRIBING THE RESULTS OF A STUDY USING DESCRIPTIVE STATISTICS AND A VARIETY OF GRAPHING TECHNIQUES.In addition to descriptive statistics, researchers use inferential statistics to draw more general conclusions about their data. In short, inferential statistics allow researchers to (a) assess just how confident they are that their results reflect what is true in the larger population and (b) assess the likelihood that their findings would still occur if their study was repeated over and over. In this chapter, we examine methods for doing so.
SAMPLES AND POPULATIONS
Inferential statistics are necessary because the results of a given study are based only on data obtained from a single sample of research participants. Researchers rarely, if ever, study entire populations; their findings are based on sample data. In addition to describing the sample data, we want to make statements ab.
In classical sampling theory, why is a model always developed on a sample even when the whole data is available? Because all the observations and variables may not be needed for developing a model, because they are not relevant for the development.
Introduction to interpreting graphical displays of data; bar graphs, histograms, stem-and-leaf plots, time series graphs, circle graphs, dot plots, and ogives.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
2. Section 7.1
What Is a Sampling Distribution?
After this section, you should be able to…
DISTINGUISH between a parameter and a statistic
DEFINE sampling distribution
DISTINGUISH between population distribution,
sampling distribution, and the distribution of sample
data
DETERMINE whether a statistic is an unbiased
estimator of a population parameter
DESCRIBE the relationship between sample size and the
variability of an estimator
3. The process of statistical inference involves using information
from a sample to draw conclusions about a wider population.
Different random samples yield different statistics.
We need to be able to describe the sampling distribution of
possible statistic values in order to perform statistical
inference. We can think of a statistic as a random variable
because it takes numerical values that describe the outcomes
of the random sampling process.
Population
Sample Collect data from a
representative Sample...
Make an Inference
about the Population.
4.
5. Parameters and Statistics
A parameter is a number that describes some characteristic of
the population. In statistical practice, the value of a parameter
is usually not known because we cannot examine the entire
population.
A statistic is a number that describes some characteristic of a
sample. The value of a statistic can be computed directly from
the sample data. We use a statistic to estimate an unknown
parameter.
6. Symbols: Parameters and Statistics
Proportions Means Standard
Deviation
Statistic s
Parameter p µ
7. Parameter v. Statistic
Identify the population, the parameter (of interest), the
sample, and the statistic in each of the following settings.
A pediatrician wants to know the 75th
percentile for the distribution of heights of 10-
year-old boys so she takes a sample of 50
patients and calculates Q3 = 56 inches.
8. Parameter v. Statistic
Population: all 10-year-old boys
Parameter: 75th percentile, or Q3
Sample: 50 10-year-old boys included in the
sample
Statistic: Q3 = 56 inches.
9. Parameter v. Statistic
Identify the population, the parameter, the sample, and
the statistic in each of the following settings.
A Pew Research Center poll asked 1102 12
to 17-year-olds in the United States if they
have a cell phone. Of the respondents, 71%
said yes.
10. Parameter v. Statistic
Population: All 12-17 year olds in the US
Parameter: Proportion with cell phones
Sample: 1102 12-17 year olds with cell phones
Statistic: 𝑝 = 71
11. Sampling Distribution
The sampling distribution of a statistic is the distribution of
values taken by the statistic in all possible samples of the
same size from the same population.
In practice, it’s difficult to take all possible samples of size n
to obtain the actual sampling distribution of a statistic.
Instead, we can use simulation to imitate the process of
taking many, many samples.
One of the uses of probability theory in statistics is to
obtain sampling distributions without simulation. We’ll get
to the theory later.
12.
13.
14. Population Distributions vs.
Sampling Distributions
There are actually three distinct distributions involved when
we sample repeatedly and measure a variable of interest.
1) The population distribution gives the values of the variable
for all the individuals in the population.
2) The distribution of sample data shows the values of the
variable for all the individuals in the sample.
3) The sampling distribution shows the statistic values from
all the possible samples of the same size from the
population.
15. Bias & Variability
Bias means that our aim is off and
we consistently miss the bull’s-eye
in the same direction. Our sample
values do not center on the
population value.
High variability means that
repeated shots are widely
scattered on the target. Repeated
samples do not give very similar
results.
16. Describing Sampling Distributions:
Center
A statistic used to estimate a parameter is an
unbiased estimator (most accurate) if the mean of
its sampling distribution is equal to the true value of
the parameter being estimated.
17. Describing Sampling Distributions:
Spread
The variability of a statistic is described by the spread of its
sampling distribution. This spread is determined primarily
by the size of the random sample. Larger samples give
smaller spread. The spread of the sampling distribution
does not depend on the size of the population, as long as
the population is at least 10 times larger than the sample.
n=100 n=1000
18. Describing Sampling Distributions:
Shape
Sampling distributions can take on many shapes. The same
statistic can have sampling distributions with different
shapes depending on the population distribution and the
sample size.
Sampling distributions for
different statistics used to
estimate the number of tanks
in German during World War 2.
The blue line represents the
true number of tanks.