According to basic probability we divide the figure of favourable outcomes by the total number of possible outcomes in our sample space. If we're observing for the chance it will rain, this will be the number of days in our record that it rained divided by the total number of similar days in our record. If our meteorologist has data for 100 days with similar weather conditions, and on 80 of these days it rained (a favourable outcome), the probability of rain on the next similar day is 80/100 or 80%.
In view of the fact that a 50% probability means that an experience is as likely to happen as not, 80%, which is greater than 50%, means that it is more likely to rain than not. But what is the probability that it won't rain Keep in mind that because the favourable outcomes represent all the possible ways that an event can occur, the sum of the different probabilities must equal 1 or 100%, so 100% - 80% = 20%, and the probability that it won't rain is 20%.
The following scatter plot with a fitted line shows that there is a positive relationship b/w selected 15 student's maths and science scores. ...
represent all the possible ways that an event can occur, the sum of the different probabilities must equal 1 or 100%, so 100% - 80% = 20%, and the probability that it won't rain is 20%.
2. The table below gives the marks of 15 students in tests in 2 subjects:
a. Scatter graph of the maths and science scores with best fitted line
The following scatter plot with a fitted line shows that there is a positive relationship b/w selected 15 student's maths and science scores. Part 2b will be the evidence to prove this hypothesis that student's math scores will be positively related to their science scores. Correlation coefficient (r = + 0.936) this magnitude shows that it's highly correlated and the positive sign shows that there is a positive correlation between the variables. So we conclude that as one variable increases other one will also increases.
b. Comment on the position of the line of best fit and any correlation between the scores.
Although one objective of correlation is a line fitted to the data, this line is not used to predict an unknown value of one variable when given a value of the other variable; it simply shows the relationship between the two variables. This best-fit line is the one that minimizes the sum of squared deviations between the points and the line, measured vertically (along the Y axis).
The Bivariate Correlations procedure computes Pearson's correlation coefficient. Correlations measure how variables or rank orders are related. Before calculating a correlation coefficient, screen your data for outliers (which can cause misleading results) and evidence of a linear relationship. Pearson's