{"id":64305,"date":"2025-02-02T12:29:12","date_gmt":"2025-02-02T12:29:12","guid":{"rendered":"https:\/\/peraltafinancing.com\/agriculture\/a-practical-guide-with-agri-analyze\/"},"modified":"2025-02-02T12:29:12","modified_gmt":"2025-02-02T12:29:12","slug":"a-practical-guide-with-agri-analyze","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=64305","title":{"rendered":"A Practical Guide with Agri Analyze"},"content":{"rendered":"


\n<\/p>\n

\n

\u00a0The blog is about Pearson correlation theory, properties, formulas of calculation, testing of correlation, solved example and step by step guide to perform correlation in Agri Analyze tool. Quiz of correlation is also shared (Reading time 12 mins)<\/i><\/p>\n

Correlation<\/strong> refers to a statistical measure that describes the extent to which two variables change together. It is the degree of linear relationship between two continuous variables in a bivariate distribution. It is a way to quantify the degree to which two variables are related.\u00a0<\/p>\n

Correlation can be positive (both variables increase or decrease together), negative (one variable increases while the other decreases), or zero (no relationship between the variables). The correlation coefficient, typically denoted as r and it ranges from -1 to 1:<\/p>\n

<\/p>\n

Where, r = 1 indicates perfect positive correlation<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0r = -1 indicates perfect negative correlation<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0r = 0 indicates no correlation<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0<\/span><\/p>\n

Properties of correlation coefficient:<\/b>\u00a0 \u00a0 \u00a0 \u00a0<\/span><\/p>\n

1) The value of correlation always ranges between -1 to +1.<\/span><\/p>\n

2) Correlation is independent of change in origin and scale.<\/span><\/p>\n

3) Correlation is unit free.<\/span><\/p>\n

4) In case of two variable frame work correlation coefficient is geometric mean of two regression coefficient.<\/span><\/p>\n

<\/p>\n

Visualizing relationship using scatter diagram: <\/span><\/b><\/p>\n

In correlation problems, first we
\nhave to investigate whether there is any relation between the variables, say, X
\nand Y. For this purpose, we use scatter diagram.<\/span><\/p>\n

From the scatter diagram, it is
\npossible to determine the presence of correlation between X and Y as well as
\nits nature whether it is positive or negative and whether it is linear or
\ncurvilinear. In the figure, diagrams a, b and c illustrate positive
\ncorrelation, negative correlation and no correlation, respectively. When the
\ntrend is linear, as shown in diagrams a and b, the relationship between X and Y
\nis referred to as linear correlation. Conversely, when the trend is
\ncurvilinear, as shown in diagram d, the relationship is termed curvilinear or
\nnon-linear correlation. This non-linear relationship can take various forms,
\nsuch as quadratic, cubic, etc. Below are the scatter diagrams for the different relationships of the variables.<\/span><\/p>\n

<\/span><\/div>\n

\n
<\/a><\/div>\n
<\/a><\/div>\n
\n

Pearson correlation:<\/span><\/b><\/p>\n

The scatter diagram will give only a
\nvague idea about the presence or absence of correlation and the nature
\n(positive or negative) of correlation. It will not indicate about the strength
\nor degree of relationship between two variables. The index of the degree of
\nrelationship between two continuous variables is known as correlation
\ncoefficient. The correlation coefficient is symbolized as r in case of a sample
\nand as\u00a0<\/span>‘rho’ in case of population. The
\ncorrelation coefficient, r is known as Pearson’s Correlation coefficient, since
\nit was developed by Karl Pearson. It is often referred to as Product-moment
\ncorrelation in order to distinguish it from other measures of
\ninter-relationship.<\/span><\/p>\n

<\/a><\/div>\n

The numerator is termed as the sum of products of X and Y and abbreviated as SP (XY). In the denominator, the first term is called the sum of squares of X or SS(X) and the second term is called the sum of squares of Y or SS(Y). The above simplified formula is used for computational purposes.<\/p>\n

\n

The denominator in the above formula is always positive. The numerator may be positive or negative, making r to be either positive or negative.<\/p>\n

The correlation coefficient r is used under certain assumptions:<\/p>\n

\n
    \n
  1. The variables under study are continuous random variables and they are normally distributed<\/li>\n
  2. The relationship between the variables is linear<\/li>\n
  3. Each pair of observation is unconnected with other pairs<\/li>\n<\/ol>\n<\/div>\n<\/div>\n
    \n

    Testing the Significance of the Correlation Coefficient: A Step-by-Step Guide<\/p>\n

    To test the significance of the correlation coefficient, typically perform a hypothesis test to determine whether the observed correlation is statistically significant. The steps for testing the significance of the correlation coefficient r are as follows:<\/p>\n

    <\/a><\/div>\n

    Solved example of Pearson Correlation<\/b><\/p>\n

    Problem statement: <\/b>There are two variables X and Y each having 5 observations. Compute the Pearson correlation and also test its significance using t test. The data is shared below<\/p>\n

    X: 10, 20, 30, 40, 50\u00a0 and Y: 20, 25, 15, 35, 30<\/p>\n<\/div>\n

    \n
    <\/a><\/div>\n

    <\/p>\n

    <\/a><\/div>\n

    Steps to perform Pearson Correlation Analysis using Agri Analyze<\/b>
    A more complex data for 4 variables is considered with 150 observations was considered for demonstration. The snap is given below:<\/span><\/p>\n

    <\/a><\/div>\n

    Link of the data set<\/a>
    Step1:\u00a0<\/b><\/span>Go with Agri Analyze
    \nsite.     Direct link\u00a0<\/a><\/span>\u00a0<\/span><\/div>\n

    Step2: <\/b>Click on\u00a0ANALYTICAL TOOL<\/b> followed by\u00a0<\/span>CORRELATION AND REGRESSION ANALYSIS<\/b> followed by\u00a0<\/span>PEARSON CORRELATION<\/b><\/span><\/p>\n

    Step3: <\/b>Upload the csv file and Click on SUBMIT<\/b> button<\/p>\n
    <\/a><\/div>\n

    <\/div>\n

    Step4: <\/b>Click on the download<\/p>\n

    Output Report:<\/b><\/p>\n

    The output will have three components 1) Heatmap 2) Correlation with p values 3) Interpretation report<\/p>\n

    1) Heatmap<\/b><\/p>\n

    \n
    <\/a><\/div>\n

    2) Correlation Matrix<\/b>

    <\/b><\/div>\n

    <\/a><\/div>\n

    3) Smart interpretation<\/b><\/p>\n

    <\/a><\/div>\n

    Additional Link<\/b><\/p>\n

    This blog is written by\u00a0<\/b><\/p>\n<\/div>\n<\/div>\n

    <\/span><\/span>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"

    \u00a0The blog is about Pearson correlation theory, properties, formulas of calculation, testing of correlation, solved example and step by step guide to perform correlation in Agri Analyze tool. Quiz of correlation is also shared (Reading time 12 mins) Correlation refers to a statistical measure that describes the extent to which two variables change together. It […]<\/p>\n","protected":false},"author":1,"featured_media":64306,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12026],"tags":[25333,24554,2059,2058],"dealstore":[],"offerexpiration":[],"class_list":["post-64305","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-agriculture","tag-agri","tag-analyze","tag-guide","tag-practical"],"yoast_head":"\nA Practical Guide with Agri Analyze - Som2ny Network<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fivemor.com\/?p=64305\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"A Practical Guide with Agri Analyze - Som2ny Network\" \/>\n<meta property=\"og:description\" content=\"\u00a0The blog is about Pearson correlation theory, properties, formulas of calculation, testing of correlation, solved example and step by step guide to perform correlation in Agri Analyze tool. 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