Our famous Junior Coyotes Summer camps have been announced for CCBC Award winner from left to right: Individual and team awards are presented.
The Coyotes were well represented on the All Conference teams as first team award winners were: Second team winners were: The Coyotes also were recognized for one of the two team awards handed out, and took home league lead in ERA 3. The Okanagan College Coyotes wrapped up their regular season play this past weekend with a 4 games set against the University of Calgary Dinos with first place on the line.
The Coyotes would wrap up the weekend coming just short by splitting the weekend games. They would finish the season in second place with a record, one game behind the Prairie Baseball Academy Dawgs. In game one on Friday, the OC offense could not get anything going as the Dinos starter would hold the Coyotes to no hits until the 7th inning.
He would eventually come around to score for the Coyotes lone run in the game. Final score Dinos. On Saturday, the Coyotes would look to rebound offensively and get back to their hitting ways.
He would give up 8 earned runs on 7 hits and would strike out 5. OC would jump out to an early lead, in after 2 innings, but the Dinos would reply with 3 runs in the top of the 3rd to take the lead.
They would add 3 more runs in the 5th inning. This would allow the OC offense to battle back. In the bottom of the 6th, OC would put up 4 runs to tie the game at OC would score a run in each of the next two innings to push the score to He would allow one run before shutting the door. Final Score Coyotes. In Game two, the Coyotes would not be able to overcome an early deficit. The Dinos offense would capitalize on their opportunities scoring 7 times in the top of the 2nd inning.
Dawson would throw 2. Suppose that your research was to assess whether height is associated with self-esteem. Because both height and scores on a measure of self-esteem can have a wide range, you have two continuous variables.
In this case, weight is a confounding variable that you need to control for. Here is another example. You might have assessed whether more females than males want to read a specific romantic novel. Here, your independent variable is gender and your dependent variable is the determination to read the book. Since gender has categories male and female , this is a categorical variable. If you have assessed the determination to read the book on a scale from 1 to 10 e.
Here, relationship status is your confounding variable. We will return to these examples throughout this blog post. At this point, it is important to remember that outlining your research in this way helps you to write up your results section in the easiest way possible.
For continuous variables, you are using descriptive statistics and reporting the measures of central tendency mean and measures of variability or spread standard deviation. For categorical variables, you are using frequencies statistics and reporting the number or frequency of participants per category and associated percentages.
Both these statistics require you to make a table, and in both cases you also need to comment upon the statistics. How does all of this look in practice? Recall the two examples that were outlined above. You need to make a table, as in TABLE 1 below, which identifies means and standard deviations for all these variables.
When commenting upon the results, you can say:. Participants were on average Note that, in this example, you are concluding that participants had moderate self-esteem levels if their self-esteem was assessed on a 1 to 10 scale. Since the value of 5 falls within the middle of this range, you are concluding that the mean value of self-esteem is moderate. If the mean value was higher e. Descriptive statistics for all variables used in research: M SD Height cm You can also outline descriptive statistics for specific groups.
Descriptive statistics for the determination to read the book, by gender: Thus, you are not reporting means and standard deviations, but frequencies and percentages. To put this another way, you are noting how many males versus females wanted to read the book and how many of them were in a relationship, as shown in TABLE 3. You can report these statistics in this way:. Frequencies statistics for all variables used in research: The first of these is correlation, which you use when you want to establish if one or more continuous, independent variables relate to another continuous, dependent variable.
The first step here is to report whether your variables are normally distributed. You do this by looking at a histogram that describes your data. If the histogram has a bell-shaped curve see purple graph below , your data is normally distributed and you need to rely on a Pearson correlation analysis. Here, you need to report the obtained r value correlation coefficient and p value which needs to be lower than. If you find a correlation, you need to say something like:.
Histogram testing the normal distribution of data: Note also that positive correlation occurs when higher levels of one variable correlate with higher levels of another variable. Negative correlation, however, occurs when higher levels of one variable correlate with lower levels of another variable. One final thing to note, which is important for all analyses, is that when your p value is indicated to be. If your data is skewed rather than normally distributed see red graphs , then you need to rely on a Spearman correlation analysis.
Here, you report the results by saying:. You also need to make a table that will summarise your main results. Correlations between all variables used in research: Height cm Self-esteem Height cm 1. Correlations between all variables used in research, before and after controlling for a covariate: These are the specific points that you need to address in order to make sure that all assumptions have been met:.
All of this may sound quite complex. But in reality it is not: Once you conclude that your assumptions have been met, you write something like:. Since none of the VIF values were below 0. Durbin-Watson statistics fell within an expected range, thus indicating that the assumption of no autocorrelation of residuals has been met as well.
Finally, the scatterplot of standardised residual on standardised predicted value did not funnel out or curve, and thus the assumptions of linearity and homoscedasticity have been met as well. If your assumptions have not been met, you need to dig a bit deeper and understand what this means. A good idea would be to read the chapter on regression and especially the part about assumptions written by Andy Field. You can access his book here. This will help you understand all you need to know about the assumptions of a regression analysis, how to test them, and what to do if they have not been met.
You have entered height and weight as predictors in the model and self-esteem as a dependent variable. First, you need to report whether the model reached significance in predicting self-esteem scores. Look at the results of an ANOVA analysis in your output and note the F value, degrees of freedom for the model and for residuals, and significance level.
You need to multiply this value by to get a percentage. Thus, if your R 2 value is. Model summary for regression: This value represents the change in the outcome associated with a unit change in the predictor.
You can report all these results in the following way:. For every increase in height by 1 cm, self-esteem increased by. Reporting the results of a chi-square analysis As we have seen, correlation and regression are done when all your variables are continuous.
Chi-square analysis, which is what we will describe here, is done when all your variables are categorical. It means that even if you need an urgent delivery, your essay will always be: Completely authentic; Structured and formatted according to the specified style; Creatively written; Researched and accordingly referenced; Meeting all requirements set forth.
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