Based on the above tables, there is no difference on the correlations between four variables in using one-tailed test and two-tailed test. 2. Regression

Model summary:
[pic]

Coefficients:
[pic]

Scatterplot and regression line:
[pic]

Conclusion:

From the first table, as the value of R square is 0.09 (i.e. R = 0.3), it means that the overall quality has positive linear relationship with expected grade (as the above scatterplot and regression line you can see), and the equation of regression is: Overall quality = 1.718 + 0.526 x Expected grade. 3. One-sample t test

Result of one-sample test:
[pic]

*We set H0: μ = 20; HA: μ ≠ 20

Conclusion:

From the above table, as the p-value is smaller than 0.05 (significant level) and the test value (20) is not within the confidence interval (23.60 – 28.82), we reject H0, i.e. we have insufficient evidence to conclude that 20 is the expected value of data.

4. Mann-Whitney Test

Result of test statistics:

[pic]

H0: No birth weight difference between prenatal care in the first trimester and third trimester

HA: Having birth weight difference between prenatal care in the first trimester and third trimester

Conclusion:

From the above table, as the p-value (0.033) is smaller than 0.05, we do not reject H0, that means we have sufficient evidence to conclude that there is no birth weight difference between prenatal care in the first trimester and third trimester. 5. Wilcoxon’s Matched Pairs Signed Ranks Test

Result of test statistics:
[pic]

H0: Before = After; HA: Before ≠ After

Conclusion:

From the above table, as the p-value (0.002) is smaller than 0.05, we reject H0, so we have insufficient evidence to conclude that there is no difference between the subjects’ weight pre and post intervention.

...X= attitude towards the city
Y= duration of residence in that city
H0 = rXY=0 i.e there is NO relationship between the 2 continuous variables X and Y
Ha ≠0, there is a relationship between the 2 continuous variables X and Y
Analyze -> correlate -> bivariate
The SPSS output indicates that rXY has a value of .936. Clearly this value of rxy seems different from zero. However the question that arises is whether this value of rxy is statistically different from zero at 95% level of confidence.
There is a significant relationship between these two variables at 95% confidence as p-value is at .000 which is below 0.05. Hence we are therefore unable to retain H0 and thus we accept HA. Thus, we can infer at 95% level of confidence that in this sample, there is indeed a significant relationship between the two variables X and Y (i.e rXY is indeed different from zero). Assuming that this sample is a good representation of the target population, we extend this inference even to the target population. Hence, even in the target population there is indeed a significant positive relationship between the two variables X and Y (i.e rXY is indeed different from zero).
2. Multiple regression write up
y= attitude towards the city of residence (dependent variable)
x1 = duration of residence in the city (1st independen variable)
x2= importance associated with the weather in the city (2nd IV)
The initial model is: Y = β1 + β2.X1+ β3.X2
Step 1:
H0: R2= 0 i.e...

...Medical Statistics Practical 1
(IBM—SPSS – Statistics)
IBM SPSS Statistics version 20 [SPSS]:
IBM SPSS [Statistical Package for the Social Sciences] formerly called SPSS is a statistical
software. There are several versions but we will use version 20.
During the practical sessions you will use SPSS to define variables, enter data and carry out
descriptive (Frequencies and Percentages, Mean +/- SD) and Inferential statistics (Chi-Square, ttest, etc.)
Where can I get SPSS?
You will need to contact Information Solution and Services (ISS)
Why do I need to learn SPSS?
If you need to answer a research question, you will need to collect data, and then enter the data
using SPSS and finally you will need to analyze the data so you could explain the meaning of the
results.
In other words you will test the hypothesis and you will be able to reject or retain the null
hypothesis. To do this you will need to decide the type of data you need to collect, how are you
going to collected it and which analysis you will use?
SECTION 1
By the end of this section you will be able to:
Define variables and enter data in the SPSS data editor.
Save data
How to Open SPSS:
After you Logon. Double click the SPSS icon OR click on Start---Programs –IBM -SPSS
Statistics 20
The following window will...

...Using SPSS for Data Analysis: Support Document for SPSS Output Tables
1
OFFICE OF PLANNING, ASSESSMENT, RESEARCH AND QUALITY
Using SPSS for Data Analysis: Support Document for SPSS Output Tables
Prepared by: UW-Stout Office of Planning, Assessment, Research & Quality (PARQ)
Tynan Heller Susan Greene Revised on 8/29/12
Prepared for: UW-Stout Campus
Report distributed to: UW-Stout Campus
DOCUMENT NO: BPA-900 APPROVAL: Susan Greene The user is responsible for ensuring this is the current revision. Thank you!
EFFECTIVE: 8/29/2012 SUPERSEDES: Ver 4
2
3 This document provides an explanation on how to read SPSS output tables for a range of analyses (Created using SPSS version 17.0)
To view a topic, please click the appropriate heading in the Table of Contents
Table of Contents
Frequency Analysis ......................................................................................................................... 5 Definitions associated with a frequency analysis ....................................................................... 5 Section 1: Frequencies without missing responses ..................................................................... 7 Section 2: Frequencies without descriptive & with missing responses ...................................... 8 Section 3: Frequencies with descriptive statistics...

...Executive Summary
The purpose of the research being undertaken is to fill a void in the literature surrounding organisations’ attitudes towards risk. The report will focus on the recent failure of the large Swiss Bank UBS; whereby an analysis of data of attitudes towards risk before and after the scandal, will give an indication on the effects the UBS bank scandal has had on financial organisations’ attitudes towards risk. In addition, through the use of correlation coefficient and regression analysis whether or not there is a correlation between the risk attitude of companies and their volatilitywill be assessed, and if so to measure this effect.
Both primary and secondary research has been used in gathering the data. The primary research was conducted by distributing a questionnaire to the CEOs; questioning how they consider their attitudes was towards risk. The question ranged from 1 to 30; where 1 is being the most conservative and 30 being the most risky. This primary research displays the financial organisations’ attitudes towards risk after the UBS scandal. For the Primary research to be conducted a sample was collected consisting of 100 CEOs from various financial organisations’. These CEOs were taken from a list of the 100 largest companies in the City of London. As the CEOs were only chosen from the city of London the data collection only represents this area; therefore cannot be used as a benchmark for other financial organisations in other regions.
The...

...phones to school. It is very reasonable because bringing phone toschool potentially disrupts the learning process. Moststudents use cell phones irresponsibly. They use cell phones to talk to their friend during class time. They also use the calculator and camera features in the class as well. Those potentially lead less concentration in the time of learning and teaching process.
Students go to school to learn and behave fair way. Mobile phones provide a large temptation to cheat in tests. They can communicate to anyone and almost anywhere in the world. Because of the small size of the cell phone, students can send a text quietly and discreetly. The text can go unnoticed anywhere to get help on answering tests, homework, and other class assignment. Learning in school is to behave fair not cheating.
Therefore, schools should ban students from bringing their cell phones. However it should be done fairly. In case of an emergency some student need a call for help, providing easy access to phone is better.
NEVER TRY SMOKING
A lot of people, especially teenagers, who do not smoke,always want to try smoking. They know it is bad for them and all, but it is just something they want to try. So they ask one of their smoker friends for a cigarette. Admittedly, they firstly can not light it on their own so they ask his friend to do it. Then they inhale that cigarette and smoke occasionally.
Apparently that makes them the born smokers. Now they do smoke fairly...

...Simple Linear Regression in SPSS
1.
STAT 314
Ten Corvettes between 1 and 6 years old were randomly selected from last year’s sales records in Virginia Beach, Virginia. The following data were obtained, where x denotes age, in years, and y denotes sales price, in hundreds of dollars. x y a. b. c. d. e. f. g. h. i. j. k. l. m. 6 125 6 115 6 130 4 160 2 219 5 150 4 190 5 163 1 260 2 260
Graph the data in a scatterplot to determine if there is a possible linear relationship. Compute and interpret the linear correlation coefficient, r. Determine the regression equation for the data. Graph the regression equation and the data points. Identify outliers and potential influential observations. Compute and interpret the coefficient of determination, r2. Obtain the residuals and create a residual plot. Decide whether it is reasonable to consider that the assumptions for regression analysis are met by the variables in questions. At the 5% significance level, do the data provide sufficient evidence to conclude that the slope of the population regression line is not 0 and, hence, that age is useful as a predictor of sales price for Corvettes? Obtain and interpret a 95% confidence interval for the slope, β, of the population regression line that relates age to sales price for Corvettes. Obtain a point estimate for the mean sales price of all 4-year-old Corvettes. Determine a 95% confidence interval for the mean sales price of all 4-year-old Corvettes. Find the...

...Human nature refers to the distinguishing characteristics, including ways of thinking, feeling and acting, that humans tend to have naturally, independently of the influence of culture. The questions of what these characteristics are, what causes them, and how fixed human nature is, are amongst the oldest and most important questions in western philosophy. These questions have particularly important implications in ethics, politics, and theology. This is partly because human nature can be regarded as both a source of norms of conduct or ways of life, as well as presenting obstacles or constraints on living a good life. The complex implications of such questions are also dealt with in art and literature, while the multiple branches of the Humanities together form an important domain of inquiry into human nature, and the question of what it is to be human.
The branches of contemporary science associated with the study of human nature include anthropology, sociology, sociobiology, and psychology, particularly evolutionary psychology, and developmental psychology. The "nature versus nurture" debate is a broadly inclusive and well-known instance of a discussion about human nature in the natural sciences.
Contents
1 History
1.1 Socratic philosophy
1.2 Modernism
1.3 Natural science
2 See also
3 References
4 Further reading
History
The concept of nature as a standard by which to make judgments was a basic presupposition...

...NEGATIVELY-KEYED ITEMS AND REVERSE-SCORING
Many psychological questionnaires include a mixture of “positively-keyed” and “negatively-keyed” items, and this needs to be addressed before computing the scores on the questionnaires and before conducting any analyses. This handout describes the distinction between positively-keyed items and negatively-keyed items, it describes the logic of reverse-scoring, and it outlines SPSS steps to reverse-score negatively keyed items.
Positively-keyed items and negatively-keyed items
Positively-keyed items are items that are phrased so that an agreement with the item represents a relatively high level of the attribute being measured. For example, a self-esteem questionnaire might include an item such as “I like myself”, which is rated on a 5-point likert scale (1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree). This item is positively-keyed, because an Agreement or Strong Agreement with the item indicates a relatively high level of self-esteem (at least as compared to a Disagreement with the item).
Negatively-keyed items are items that are phrased so that an agreement with the item represents a relatively low level of the attribute being measured. For example, a self-esteem questionnaire might include an item such as “I dislike myself”, rated on the same 5-point scale (1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree). This item is...

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