{"id":712,"date":"2017-08-03T09:09:34","date_gmt":"2017-08-03T08:09:34","guid":{"rendered":"https:\/\/marriott-stats.com\/nigels-blog\/?p=712"},"modified":"2019-02-09T10:34:53","modified_gmt":"2019-02-09T10:34:53","slug":"uk-weather-tracker-6-july-2017","status":"publish","type":"post","link":"https:\/\/marriott-stats.com\/nigels-blog\/uk-weather-tracker-6-july-2017\/","title":{"rendered":"UK Weather Tracker #6 – July 2017"},"content":{"rendered":"

My latest UK weather tracker shows that July has been an unremarkable month and it would appear that the summer in 2017 will also be unremarkable.\u00a0 Believe it or not, I have predicting this for the last few years and I will explain why in a separate post in September.<\/p>\n

As always, the meteorological year starts in December which is why\u00a0July appears as the 8th data point in the charts below.\u00a0 More information on the layout of the charts can be found in this post.<\/a><\/p>\n

\"\"<\/p>\n

July is the first month this year where all 6 variables shown in the chart were between the historical upper & lower deciles so it should be no surprise to hear me describe July as an unremarkable month.\u00a0 In fact, “unremarkable” months are actually more remarkable than you might think.\u00a0 Consider the definition of a decile.\u00a0 The probability of a month being lower than the lower decile is 10% by definition and conversely the probability of being above the upper decile is also 10%.\u00a0 This means there is an 80% chance of any month being between the upper and lower deciles.\u00a0 If each variable has\u00a0a 4 in 5 chance of being between the deciles, what is the probability of all 6 being between the deciles?<\/p>\n

Before calculating this, we need to assume that the 6\u00a0 variables have no relationship with each other which is a dangerous assumption to make and one that I will be exploring in September but for now, I will make this so.\u00a0 Then the probability of two variable being between the deciles will 4\/5 x 4\/5 or 0.8^2.\u00a0 With 6 variables, the equivalent calculation is 0.8^6 which is equal to 0.262 or 26.2%.\u00a0 So an “unremarkable” month would be expected to occur once in every 4 months which is actually not that common and therefore should be regarded as remarkable rather than unremarkable!<\/p>\n

The converse of this is that for 3 in 4 months, we can expect at least of the variables to have an “uncommon” value perhaps even a near record value.\u00a0 This is a good demonstration of the dangers of analysing MULTIVARIATE data sets.\u00a0 If you are tracking many indicators at the same time, the odds of all indicators being unremarkable are in fact quite low.\u00a0 Yet the human tendency is to observe unusual values and to ask why is that happening?\u00a0 In fact, nothing is happening except normal chance.\u00a0 This is a variant of the false positive fallacy that humans are prone to.<\/p>\n

Economic indicators are especially prone to this illusion since there are so many indicators available and if you are determined to present a positive or negative picture of the current economic climate, then you can nearly always find an indicator or indicators that will prove your point.\u00a0 Such claims are quite common these days with the debate about the effects of Brexit and I intend to start an economic indicator tracker in the near future to help people to make sense of the latest economic statistics.<\/p>\n","protected":false},"excerpt":{"rendered":"

My latest UK weather tracker shows that July has been an unremarkable month and it would appear that the summer in 2017 will also be unremarkable.\u00a0 Believe it or not, I have predicting this for the last few years and I will explain why in a separate post in September.<\/p>\n","protected":false},"author":3,"featured_media":668,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","footnotes":""},"categories":[90],"tags":[40,34,57],"class_list":{"0":"post-712","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-archive","8":"tag-presenting-data","9":"tag-weather","10":"tag-weather-tracker","11":"entry","12":"override"},"_links":{"self":[{"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/posts\/712","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/comments?post=712"}],"version-history":[{"count":1,"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/posts\/712\/revisions"}],"predecessor-version":[{"id":714,"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/posts\/712\/revisions\/714"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/media\/668"}],"wp:attachment":[{"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/media?parent=712"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/categories?post=712"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/tags?post=712"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}