Survey project
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1) A survey was conducted of students at B.P.H.S that collected data on time spent studying, classes failed, AP/honors classes taken, and opinions on academic honesty. 2) Statistical analysis was performed on the data including confidence intervals, two-sample t-tests, and chi-square tests. 3) The results of the statistical tests showed that there was no significant difference between males and females in time spent studying, classes failed, or AP/honors classes taken. The tests also showed that students' opinions on academic honesty issues were independent of grade level.
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Week 6 DQ1. What is your research questionIs there a differen.docx
Week 6 DQ
1. What is your research question?
Is there a difference between the math utility of a male and a female?
2. What is the null hypothesis for your question?
Hn There is no difference in the math utility between male and female.
Alternative hypotheses can also be created in the case the null hypothesis is proven incorrect. Two alternative hypotheses are:
Ha1 Feales have a higher math utility.
Ha2 Males have a higher math utility.
3. What research design would align with this question?
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4. What comparison of means test was used to answer the question (be sure to defend the use of the test using the article you found in your search)?
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5. What dependent variable was used and how is it measured?
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Group Statistics
T1 Student's sex
N
Mean
Std. Deviation
Std. Error Mean
T1 Scale of student's mathematics utility
Male
9453
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1.01962
.01049
Female
9349
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Levene's Test for Equality of Variances
t-test for Equality of Means
F
Sig.
t
df
Sig. (2-tailed)
Mean Difference
Std. Error Difference
95% Confidence Interval of the Difference
Lower
Upper
T1 Scale of student's mathematics utility
Equal variances assumed
17.400
.000
4.276
18800
.000
.06216
.01454
.03367
.09066
Equal variances not assumed
4.277
18775.932
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.06216
.01453
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University high school longitudinal study dataset. (2009).
References
Frankfort-Nachmias, C., & Leon-Guerrero, A. (2015). Social statistics for a diverse society (7th ed.). Thousand Oaks, CA: Sage Publications.
University high school longitudinal study dataset. (2009). Retrieved from class.waldenu.edu
The t Test for Related Samples
The t Test for Related Samples
Program Transcript
MAT.
Argument Maps Improve Critical Thinking
Argument maps improve critical thinking by more than tripling gains compared to other methods, according to the author's experience and empirical studies. The key aspect is intensive practice constructing and evaluating argument maps on a computer, which helps students precisely identify errors in their reasoning. While the studies have limitations, they suggest computer-based argument mapping significantly enhances critical thinking over one year of typical university education.
AP Statistics Survey
This document appears to be a statistics project analyzing survey results from 75 high school students. It includes 10 survey questions, confidence intervals for responses, and hypothesis tests analyzing responses. Key details include: confidence intervals for how many classes students like and activities with friends; a hypothesis test finding high school is stressful; and Chi-square tests finding being in love and fitting a fist in one's mouth depend on grade level.
Swayam
This document discusses parametric tests and provides examples of the z-test. It begins with an introduction stating the name, affiliation, and course of Sakshi Maheshwari from Banaras Hindu University. It then discusses when parametric tests are appropriate to use and lists some common parametric tests like ANOVA and Pearson's correlation. The document focuses on explaining the z-test, including how it is used to test differences between means or proportions. It provides the formulas and steps for one and two sample z-tests along with examples comparing student IQ scores and differences in child weight and swine flu mortality between groups.
Applied statistics lecture_3
This document provides an introduction to applied statistics and statistical methods. It discusses objectives such as going beyond the mean and tests of differences like t-tests and ANOVA. It also covers descriptive statistics such as measures of central tendency and dispersion, inferential statistics like z-scores and confidence intervals, and tests of differences including independent and paired t-tests and nonparametric alternatives.
One-Sample Hypothesis Tests
A researcher tested the effectiveness of an herbal supplement on physical fitness using the Marine Physical Fitness Test. 25 college students took the supplement for 6 weeks and averaged a score of 38.68 on the test, compared to the average population score of 35. Using a t-test with α=.05 and df=24, the researcher found the average score of 38.68 was not significantly different than the population mean of 35 (t=2.041, p=.052). Therefore, there is not enough evidence to conclude the supplement had an effect on fitness levels, as the higher average score could be due to chance for this small sample.
Day 11 t test for independent samples
The slides discuss comparing two means to ascertain which mean is of greater statistical significance. In these slides we will learn about three research questions in which the t-test can be used to analyze the data and compare the means from two independent groups, two paired samples, and a sample and a population.
Analyses of Survey’s Statistics
The document analyzes statistics from a survey administered to Baldwin Park High School students. It provides confidence intervals for survey questions asking about relationships, beliefs, and preferences. Hypothesis tests are conducted to compare survey results to larger studies and compare responses between genders. Chi-square tests determine whether certain opinions vary with age. While some opinions differed significantly between genders or with prior studies, age was found to not influence most beliefs and behaviors examined.
Inferential Statistics (Melrose High School Library)
A slideshow walkthrough on choosing the correct inferential statistics test for students in AP Research at Melrose High School Library. Includes instructions for using Stapplet to perform a Chi square, Z test, t test, ANOVA, and several other inferential statistical tests.
Statistical-Tests-and-Hypothesis-Testing.pptx
Here are the steps to solve this hypothesis testing problem:
1. State the null and alternative hypotheses:
H0: There is no significant difference between the means under stress and no stress conditions.
H1: There is a significant difference between the means under stress and no stress conditions.
2. Choose the level of significance: Given as α = 0.01
3. Select the appropriate statistical test: Since this involves comparing the means of two independent groups, use a two-sample t-test.
4. Compute the test statistic and p-value: Follow the t-test formula and calculation.
5. Make a decision: Reject H0 if p-value < α, fail to reject H0 if
One Sample T Test
1. The document discusses hypothesis testing using a one-sample t-test when the population variance is unknown.
2. It provides examples of when to use a z-test or t-test, and walks through the steps of conducting a one-sample t-test including stating hypotheses, determining critical values, computing test statistics, and making conclusions.
3. An example problem demonstrates these steps, testing if a therapy reduces test anxiety below a population mean of 20, finding the sample mean is significantly lower.
Stat 1
Hypothesis testing is a statistical method used to make decisions or judgments about populations based on sample data. There are 6 main steps: 1) formulate the null and alternative hypotheses, 2) specify the significance level, 3) determine the critical value, 4) compute the test statistic, 5) make a decision, 6) consider type I and II errors. The p-value is used along with the significance level to determine whether to reject or fail to reject the null hypothesis. Various statistical tests are used depending on the type of data and assumptions. Sample problems demonstrate applications of hypothesis testing across different statistical test formulas.
Unit. 6.doc
1. The document discusses classical test theory and item response theory, which are two major psychometric theories for evaluating psychological tests. Classical test theory is based on the concept of true score and examines item difficulty, discrimination, and reliability. Item response theory uses item characteristic curves to model the relationship between examinee ability and item responses.
2. The document provides formulas and interpretations for calculating item difficulty, discrimination, and optimal difficulty levels under classical test theory. Item difficulty refers to the percentage of examinees answering the item correctly, while discrimination refers to how well an item distinguishes high- and low-scoring examinees.
3. An example is given to demonstrate calculating difficulty and discrimination indices for multiple choice test items based on student
Quantitative analysis in language research
Here are the analyses for each case:
Case A: Cross-tabulation and chi-square test since it involves counting students in categories defined by gender and track preference (both nominal variables).
Case B: One-way ANOVA since it involves comparing the mean attitude scores of 3 ethnic groups (nominal IV with 3 levels) on the Likert scale questionnaire (interval DV).
Case C: Independent t-test since it involves comparing the mean English exposure scores of 2 groups defined by parents' English proficiency (nominal IV with 2 levels) on the interval scale questionnaire.
Case D: Mann-Whitney U test since it involves comparing the rankings of 2 groups defined by where they studied (nominal IV)
Successful learning what psychology says about learning better - november 4
Mark A. Laumakis gave a presentation on successful learning based on psychological science. He discussed how students primarily use ineffective study strategies like rereading and cramming. However, research shows that self-testing through retrieval practice leads to better long-term retention than passive rereading. Laumakis also presented evidence that using adaptive quizzing software in intro psychology courses improved student quiz scores, the number of questions completed, and overall course grades compared to traditional online quizzing. He concluded by emphasizing the benefits of retrieval practice, spacing out study sessions, and testing over rereading for effective learning according to psychological research.
statistic scs.pptx
This document contains information about different statistical concepts including sampling methods, inferences about population means, time series analysis, and index numbers. It discusses simple random sampling, systematic sampling, stratified sampling, and cluster sampling. It also covers estimating population parameters using confidence intervals and the standard error. The document provides examples of calculating confidence intervals for population means when the population standard deviation is known and unknown. It defines different components of a time series including trend, cycle, season, and irregular components. Finally, it outlines various index number formulas including unweighted, weighted by quantity, and weighted by price indexes.
CTA Algebra Comparative Pilot Study
This study compared the effectiveness of traditional instruction versus cognitive task analysis (CTA)-guided instruction for teaching students to solve quadratic equations by factorization. Eighty-nine high school students were split into an experimental group taught with CTA techniques following a protocol (n=29), and a control group taught with traditional methods (n=60). On a post-assessment, the experimental group scored significantly higher (mean=9.2) than the control group (mean=6.6), with an effect size of 1.19. The results provide evidence that CTA-guided instruction following a gold standard protocol improves students' ability to solve quadratic equations compared to traditional instruction.
Q4_Understanding-Hypothesis_w1.pptx
Here are the hypotheses for the examples in symbols:
1. H0: μ = 330 ml
H1: μ < 330 ml
2. H0: μ = 48 months
H1: μ > 48 months
3. H0: μ = P200,000.00
H1: μ > P200,000.00
The null hypothesis states what is being claimed or assumed to be true. The alternative hypothesis states the expected or predicted outcome if the claim is not true.
Similar to Survey project (20)
Week 6 DQ1. What is your research questionIs there a differen.docx
Week 6 DQ1. What is your research questionIs there a differen.docx
Inferential Statistics (Melrose High School Library)
Inferential Statistics (Melrose High School Library)
Successful learning what psychology says about learning better - november 4
Successful learning what psychology says about learning better - november 4
Survey project
- 1. Survey & Statistical Analysis By: Rebekah Mickle, Eva Tram, & Richard Gutierrez
- 2. Survey Gender: ______ Grade: ______ Age: ____ How many minutes do you spend studying for a test? ________ How many classes are you failing? ______ How many Advanced Placement/Honors classes are you taking? _______ Circle if you agree or disagree It is OK to copy homework from others. Agree or Disagree It is OK to test or listen to music during class time. Agree or Disagree Circle the best choice It is better to (Study the day before the test OR Study many times throughout the week before the test)
- 3. Confidence Intervals 28.59 ± (1.99)(31.70/(78^(.05))) We are 95% confident the true mean for minutes a student studies for a test at B.P.H.S is between 21.31 and 35.87 minutes. 0.40 ± (1.99)(1.14/(78^(.05))) We are 95% confident the true mean for the number of classes students at B.P.H.S are failing is between .13 and .67. 1.89 ± (1.99)(2.78/(79^(0.05))) We are 95% confident that the true mean for the number of Advanced Placement/ Honors classes students at B.P.H.S are taking is between 1.27 and 2.51.
- 5. Male Sample average=28.9, Male Sample Standard Deviation=34.1, Male Sample size=36
- 6. Female Sample average= 27.5, Female Sample Standard Deviation=29.0, Female Sample size=44With a P-Vale of 0.846, we fail to reject the null hypothesis. The average number of minutes spent studying is equal for males and females.
- 8. Male Sample average=0.486, Male Sample Standard Deviation=1.46, Male Sample size=36
- 9. Female Sample average=0.326, Female Sample Standard Deviation=0.81, Female Sample size=44With a P-Vale of 0.600 we fail to reject the null hypothesis. The average classes a student is failing is equal for males and females.
- 11. Male Sample average=1.02, Male Sample Standard Deviation=1.29, Male Sample size=36
- 12. Female Sample average=1.341, Female Sample Standard Deviation=1.256, Female Sample size=45With a P-Vale of 0.264 we fail to reject the null hypothesis. The average Advanced Placement/Honors classes for males is equal to that of the females.
- 13. Chi2:Q7 H0:Opinions are independent from grade level. Ha: Opinions are not independent from grade level. Assumptions/Conditions: Random: Sample was randomly chosen. Independent: One students opinion is independent from another's. Conduct a Chi2 test Σ(Observed-Expected)2 d.f=(2 − 1)(2 − 1) = 1 Expected 5.χ2=0.015, With a P-value as high as 0.901, we fail to reject that students feel it is not okay to copy homework regardless of grade level.
- 14. Chi2:Q8 H0:Opinions are independent from grade level. Ha: Opinions are not independent from grade level. Assumptions/Conditions: Random: Sample was randomly chosen. Independent: One students opinion is independent from another's. Conduct a Chi2 test Σ(Observed-Expected)2d.f=(2 − 1)(2 − 1) = 1 Expected 5. 5.χ2=0.000469, With a P-value as high as 0.945, we fail to reject that students feel it is not okay to listen to music during class time regardless of grade level.
- 15. Chi2:Q9 H0:Opinions are independent from grade level. Ha: Opinions are not independent from grade level. Assumptions/Conditions: Random: Sample was randomly chosen. Independent: One students opinion is independent from another's. Conduct a Chi2 test Σ(Observed-Expected)2d.f=(2 − 1)(2 − 1) = 1 Expected 5. 5.χ2=0.173, With a P-value as high as 0.677, we fail to reject that students feel it is better to study for a test throughout the week before the test regardless of grade level.