Statistics Essay Example | Topics and Well Written Essays - 4750 words

Statistics - Essay ExampleAccording to basic chance we divide the get into of favourable outcomes by the total bet of possible outcomes in our sample space. If were observing for the chance it provide 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 blow 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 lie with is as promising 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 wont rain Keep in mind that because the favourable outcomes represent completely the possible ways that an event can occur, the sum of the contrary probabilities must equal 1 or 100%, so 100% - 80% = 20%, and the probability that it wont rain is 2 0%.The following disassemble plot with a fitted line shows that there is a positive relationship b/w selected 15 students maths and wisdom 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 wont rain is 20%.2. The table below gives the marks of 15 students in tests in 2 subjects Students123456789101112131415Maths294527193946253839434921384637Science364231264241254140404323394538a. 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 students maths and science scores. Part 2b will be the evidence to prove this hypothesis that students math scores will be positively related to their science scores. Correlation coefficient (r = + 0.936) this magnitude shows that its highly correlated and the positive sign shows that there is a positive cor relation coefficient coefficient between the variables. So we conclude that as unrivaled variable increases other one will similarly 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 mensurate 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 Pearsons 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. Pearsons