General:<\/p>\n
\u00b7 Check that you included everything that was asked for in the report (not just the workbook). If you missed out computing or discussing e.g. the p-values I couldn\u2019t give you marks for those!<\/p>\n
Data description \u2013 univariate<\/p>\n
\u00b7 Things I looked for were<\/p>\n
\u00b7 A brief introduction to the variables: what do the quantities mean?<\/p>\n
\u00b7 Use of descriptive statistics to describe the main features of the data e.g. IQR, CV, mean, median, quartiles, standard deviation (together with empirical rule or Chebyshev theorem)<\/p>\n
\u00b7 A little discussion about outliers that were interesting or were removed<\/p>\n
\u00b7 Histograms, polygons, boxplots and\/or normal probability plots and discussion of the data distribution shape<\/p>\n
\u00b7 If there was no observation in a data set for a country it should not be treated as zero. Also be suspicious of zeros that do appear in the raw data set \u2013 they might have ended up there in place of no observation.<\/p>\n
\u00b7 Be careful with units \u2013 state what the units of the variables are and keep using those units for things like mean, standard deviation etc. E.g. cereal yield is in tonnes per capita, not percentage.<\/p>\n
\u00b7 Left skewed or negatively skewed data has a peak near the top of the distribution and a long lower tail.<\/p>\n
\u00b7 A data set does not have to fall into {left skewed, symmetric\/normal, right skewed}. There are many other variations (without specific names, you could just call it asymmetric for instance).<\/p>\n
\u00b7 It wasn\u2019t necessary to transform a data set for the univariate analysis if it was skewed, only if it was so skewed or outliers were so extreme that boxplots etc. were not useful. It could of course be useful for bivariate analysis if one or both variables seem to be lognormal, because then you could still see a linear relation in the scatterplot using the logged variable(s) and linear correlation and regression would be valid.<\/p>\n
Data description \u2013 bivariate<\/p>\n
\u00b7 Things I looked for were<\/p>\n
\u00b7 A scatterplot and associated discussion (not a line chart or bar chart with series side by side)<\/p>\n
\u00b7 The correlation coefficient being stated and interpreted<\/p>\n
\u00b7 Discussion about why there might or might not be a relationship<\/p>\n
\u00b7 You shouldn\u2019t just rely on the correlation coefficient \u2013 if the scatterplot indicates almost no relationship then you should say it is doubtful there is an actual relationship<\/p>\n
\u00b7 If the scatterplot indicates there is a relationship, but it is more likely to be non-linear, you should mention this.<\/p>\n
\u00b7 Don\u2019t just say there might be a third variable and leave it at that. Some discussion is important to show you know what this actually means.<\/p>\n
Confidence intervals<\/p>\n
\u00b7 Things I looked for were<\/p>\n
\u00b7 Statement of each confidence interval in a sentence, in the context of the variable<\/p>\n
\u00b7 Discussion of why the confidence intervals were valid (appealing to CLT)<\/p>\n
\u00b7 Some said the sample mean was a good estimate because it was inside the confidence interval. Of course, the sample mean is at the center of the interval! This does not guarantee anything about the accuracy of the estimate.<\/p>\n
\u00b7 If you have dropped countries from the data set, the confidence interval only estimates the mean of the population you have sampled from, which may not be the same population that the countries you dropped come from. E.g. if you drop many African countries from the data set due to lack of data, then you should use caution saying that the confidence interval estimate for the mean still applies to African countries (i.e. only valid if you are quite sure there is no systematic difference between such countries and those included in your sample in the context of your variable.<\/p>\n
P-values<\/p>\n
\u00b7 Things I looked for were<\/p>\n
\u00b7 Computation of two-tail p-values in the workbook<\/p>\n
\u00b7 An interpretation of the two-tail (not one-tail) p-value for each variable in terms of how likely it is to observe the sample estimate if the assumed population parameter was correct (not in terms of rejection of the null hypothesis or not).<\/p>\n
\u00b7 Don\u2019t guess the parameter value to be equal to the sample mean, otherwise the p-value is guaranteed to be 1 (and is just cheating!)<\/p>\n
Professional appearance<\/p>\n
\u00b7 The report should have been easy to read, well-structured, and include charts with titles and axis labels etc.<\/p>\n
\u00b7 You shouldn\u2019t put bar charts of all the countries for each variable into the report \u2013 if you want to highlight certain countries e.g. top 5 and bottom 5 just put them into a bar chart.<\/p>\n
\u00b7 You also shouldn\u2019t paste in whole descriptive statistics tables or give values with all the decimal places Excel gives you by default. A good rule of thumb is two or three significant figures. E.g. 542,997 becomes 543,000 and 0.024897874 becomes 0.025 or 0.0249. Also don\u2019t paste in the confidence interval or p-value calculation templates. Managers don\u2019t have time or understanding for technical output. They won\u2019t be impressed; they may just be confused or think you are showing off. They will be impressed if you can boil that technical material down in such a way that they can understand the key points enough to know what decisions need to be made.<\/p>\n","protected":false},"excerpt":{"rendered":"
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