One-way analysis of variance (one-way ANOVA) is a technique that is used to determine whether there are statistically significant differences between the means of two or more samples (using the F distribution) when there is only one independent variable. In this case, we used a one-way ANOVA to understand whether students' thoughts on those immigration questions differed based on ethnicity (dividing ethnicty into three indepedent groups (Asian, Hispanic and White students). So, we have three categories, Asian, Hispanic, and Asian. So, our X variable is the ethnicity, and its categorical. The outcome is their opinions on immigration questions, which in this case, it's a one to five, where five is strongly agree, one is strongly disagree. So,
For this statistical inference, the question was whether the means were truly different or could they have been samples from the same population. To do draw a conclusion, we must first assume normal distribution. We must also set the null hypothesis to m1 - m2 = 0. And per this assignment we must set the a-level at .05 and the hypothesis alternative to m1 - m2 ¹ 0; thus requiring a two-tailed test.
For this section, this is where things get a little harder to explain. The above chart represents what we call the descriptive for the one-way ANOVA. This form helps us to conduct the analyzation of the above chart so that we can determine the standard deviation and the mean scores for the quiz 3 and each section. Therefore, once we know what the section is we can show their
Inferential Statistics has two approaches for making inferences about parameters. The first approach is the parametric method. The parametric method either knows or assumes that the data comes from a known type of probability distribution. There are many well-known distributions that parametric methods can be used, such as the Normal distribution, Chi-Square distribution, and the Student T distribution. If the underlying distribution is known, then the data can be tested accordingly. However, most data does not have a known underlying distribution. In order to test the data parametrically, there must be certain assumptions made. Some assumptions are all populations must be normal or at least same distribution, and all populations must have the same error variance. If these assumptions are correct, the parametric test will yield more accurate and precise estimates of the parameters being tested. If these assumptions are incorrect, the test will have a very low statistical power. This will reduce the probability of rejecting the null hypothesis when the alternative hypothesis is true. So what happens with the data is definitely known not to fit any distribution? This is when nonparametric methods are used.
A sample of children ranging from 4 to 13 years old are going to be asked to watch a Rainbow Brite video. The children will be randomly picked from a childcare center. To ensure that the children are going to be randomly assigned, the children will range in different age groups. The first group will consist of 4, 6, and 8 year olds. The second group will consist of 10,12, and 14 year olds. It would have to be a field experiment because you have to go out and collect the data.
In this analysis the null hypothesis is that the variable are independent, in other words whether or not a person has gotten their flu shot is unrelated to which group they are in. The Alternate hypothesis is that whether or not a person has gotten their flu shot is related to which group they were placed in, the variables are not independent. The results of this analysis are that the chi-square value is 4.1620049, which is nonsignificant according to the table on page 416 of the text which shows that the level of significance for 2 degrees of freedom is 5.99. The p value of 0.1248 is also indicative of a nonsignificant result. Based on the results of this analysis and the resulting significance the keep the null hypothesis.
Collected data were subjected to analysis of variance using the SAS (9.1, SAS institute, 2004) statistical software package. Statistical assessments of differences between mean values were performed by the LSD test at P = 0.05.
...ne’s level of interest. The independent variables are the three different groups that are being studied. The ratings given by the participants will represent the dependent variables. The alpha is set as 0.05. According to SPSS, the results show that this study has a significance level of 0.000, which is less than 0.05. Because of this difference, it is appropriate to accept the research hypothesis and to reject the null hypothesis.
These student were placed into four groups. In each group, they were asked to fill out what their impressions and feelings were of the experimenter and to answer eight questions. The subject had a choice to answer the questions while being interviewed by the experimenter. Each group had the same task, however in group one, the subject was the control who had to answer the questions. In group two, the subject would answer the question and the experimenter would touch the subjects back. The third group had to answer the questions, but the experimenter would reveal information about himself and self-disclose before the subject would answer the questions. On the fourth group, the experimenter would self-disclose and proceed with the same procedures as group three, but for this group, the experimenter would touch the subject in the back. After the experiment, the subjects were asked to fill out their impressions and feelings. The independent variable for experiment two was the touching and the self-disclosure of the experimenter. The dependent variable was the time spent and duration of the subject’s self-disclosure.
The study consisted of 3200 participants (all men) .They all were given questionnaires and from their responses and their manner, each participant was put into one of two groups:
The study design I’ve created consists of data from watching two news networks. My first step was watching the two news stations, Fox News and MSNBC. The reason why I chose these two news channels was because they represent different political parties. Fox News is known to be conservative, and MSNBC is known to be liberal. Since they have different ideologies, their rhetoric upon issues representing minorities would represent their political party.
On the other hand, Quantitative research refers to “variance theory” where quantity describes the research in terms of statistical relationships between different variables (Maxwell, 2013). Quantitative research answers the questions “how much” or “how many?” Quantitative research is an objective, deductive process and is used to quantify attitudes, opinions, behaviors, and other defined variables with generalized results from a larger sample population. Much more structured than qualitative research, quantitative data collection methods include various forms of surveys, personal interviews and telephone interviews, polls, and systematic observations. Methods can be considered “cookie cutter” with a predetermined starting point and a fixed sequence of
In order to test this hypothesis 60 students will be randomly recruited. In order to get my 60 participants, I will pick students who id begins with the numbers 08. A total of 30 females and 30 males will be chosen, all psychology undergraduate students from Texas A&M International University, largely in the age range 20-25 years. No payment, other than receive 5 points of extra credit, will be offered for participation.
of 50 students (25 girls, 25 boys) from year 7. I have data from a
Whether or not people notice the importance of statistics, people is using them in their everyday life. Statistics have been more and more important for different cohorts of people from a farmer to an academician and a politician. For example, Cambodian famers produce an average of three tons or rice per hectare, about eighty per cent of Cambodian population is a farmer, at least two million people support party A, and so on. According to the University of Melbourne, statistics are about to make conclusive estimates about the present or to predict the future (The University of Melbourne, 2009). Because of their significance, statistics are used for different purposes. Statistics are not always trustable, yet they depend on their reliable factors such as sample, data collection methods and sources of data. This essay will discuss how people can use statistics to present facts or to delude others. Then, it will discuss some of the criteria for a reliable statistic interpretation.
Analysis of variance (ANOVA) is a collection of statistical models used in order to analyze the differences between group means and their associated procedures. In the ANOVA setting, the observed variance in a particular variable is partitioned into components attributable to different sources of variation. The following equation is the Fundamental Analysis-of-Variance Identity for a regression model.