Tableau – Line Chart ”; Previous Next In a line chart, a measure and a dimension are taken along the two axes of the chart area. The pair of values for each observation becomes a point and the joining of all these points create a line showing the variation or relationship between the dimensions and measures chosen. Simple Line Chart Choose one dimension and one measure to create a simple line chart. Drag the dimension Ship Mode to Columns Shelf and Sales to the Rows shelf. Choose the Line chart from the Marks card. You will get the following line chart, which shows the variation of Sales for different Ship modes. Multiple Measure Line Chart You can use one dimension with two or more measures in a line chart. This will produce multiple line charts, each in one pane. Each pane represents the variation of the dimension with one of the measures. Line Chart with Label Each of the points making the line chart can be labeled to make the values of the measure visible. In this case, drop another measure Profit Ratio into the labels pane in the Marks card. Choose average as the aggregation and you will get the following chart showing the labels. Print Page Previous Next Advertisements ”;
Category: Big Data & Analytics
Tableau – Formatting
Tableau – Formatting ”; Previous Next Tableau has a very wide variety of formatting options to change the appearance of the visualizations created. You can modify nearly every aspect such as font, color, size, layout, etc. You can format both the content and containers like tables, labels of axes, and workbook theme, etc. The following diagram shows the Format Menu which lists the options. In this chapter, you will touch upon some of the frequently used formatting options. Formatting the Axes You can create a simple bar chart by dragging and dropping the dimension Sub-Category into the Columns Shelf and the measure Profit into the Rows shelf. Click the vertical axis and highlight it. Then right-click and choose format. Change the Font Click the font drop-down in the Format bar, which appears on the left. Choose the font type as Arial and size as 8pt. as shown in the following screenshot. Change the Shade and Alignment You can also change the orientation of the values in the axes as well as the shading color as shown in the following screenshot. Format Borders Consider a crosstab chart with Sub-Category in the Columns shelf and State in the Rows shelf. Now, you can change the borders of the crosstab table created by using the formatting options. Right-click on crosstab chart and choose Format. The Format Borders appear in the left pane. Choose the options as shown in the following screenshot. Print Page Previous Next Advertisements ”;
Statistics – Data collection – Observation ”; Previous Next Observation is a popular method of data collection in behavioral sciences. The power, observation has been summed by W.L. Prosser as follows “there is still no man that would not accept dog tracks in the mud against the sworn testimony of a hundred eye witnesses that no dog had passed by.” Observation refers to the monitoring and recording of behavioral and non behavioral activities and conditions in a systematic manner to obtain information about the phenomena of interest, ”Behavioral Observation” is: Non verbal analysis like body movement. eye movement. Linguistic analysis which includes observing sounds like ohs! and abs! Extra linguistic analysis which observes the pitch timbre, rate of speaking etc. Spatial analysis about how people relate to each other. The non behavioral observation is an analysis of records e.g. newspaper archives, physical condition analysis such as checking the quality of grains in gunny bags and process analysis which includes observing any process. Observation can be classified into various, categories. Type of Observation Structured Vs. Unstructured Observation – In structured observation the problem has been clearly defined, hence the behavior to be observed and the method by which it will be measured is specified beforehand in detail. This reduces the chances of observer introducing observer”s bias in research e.g. study of p1ant safety compliance can be observed in a structure manner. Unstructured analysis is used in situations where the problem has not been clearly defined hence it cannot be pre specified that what is to be observed. Hence a researcher monitors all relevant phenomena and a great deal of flexibility is allowed in terms of what they note and record e.g. the student”s behavior in a class would require monitoring their total behavior in the class environment. The data collected through unstructured analysis should be analyzed carefully so that no bias is introduced. Disguised Vs. Undisguised Observation – This classification has been done on the basis of whether the subjects should know that they are being observed or not. In disguised observation, the subjects are unaware of the facts that they are being observed. Their behavior is observed using hidden cameras, one way mirrors, or other devices. Since the subjects are unaware that they are being observed hence they behave in a natural way. The drawback is that it may take long hours of observation before the subjects display the phenomena of interest. Disguised observation may be: Direct observation when the behavior is observed by the researcher himself personally. Indirect observation which is the effect or the result of the behavior that is observed. In undisguised observation, the subjects are aware that they are being observed. In this type of observation, there is the fear that the subject might show a typical activity. The entry of observer may upset the subject, but for how long this disruption will exist cannot be said conclusively. Studies have shown that such descriptions are short-lived and the subjects soon resume normal behavior. Participant vs. Non-Participant Observation – If the observer participates in the situation while observing it is termed as participant observation. g. a researcher studying the life style of slum dwellers, following participant observation, will himself stay in slums. His role as an observer may be concealed or revealed. By becoming a part of the setting he is able to observe in an insightful manner. A problem that arises out of this method is that the observer may become sympathetic to the subjects and would have problem in viewing his research objectively. In case of non-participant observation, the observer remains outside the setting and does not involve himself or participate in the situation. Natural vs. Contrived Observation. – In natural observation the behavior is observed as it takes place in the actual setting e.g. the consumer preferences observed directly at Pizza Hut where consumers are ordering pizza. The advantage of this method is that the true results are obtained, but it is expensive and time consuming method. In contrived observation, the phenomena is observed in an artificial or simulated setting e.g. the consumers instead of being observed in a restaurant are made to order in a setting that looks like a restaurant but is not an actual one. This type of observation has the advantage of being over in a short time and recording of behavior is easily done. However, since the consumer”s are conscious of their setting they may not show actual behavior. Classification on the Basis of Mode of Administration – This includes: monitors and records the behavior as it occurs. The recording is done on an observation schedule. The personal observation not only records what, has been specified but also identifies and records unexpected behaviors that defy pre-established response categories. Mechanical Observation – Mechanical devices, instead of human”s are to record the behavior. The devices record the behavior as it occurs and data is sorted and analyzed later on. Apart from cameras, other devices are galvanometer which measures the emotional arousal induced by an exposure to a specific stimuli, audiometer and people meter that record which channel on TV is being viewed with the latter also recording who is viewing the channel, coulometer records the eye movement etc. Audit – It is the process of obtaining information by physical examination of data. The audit, which is a count of physical objects, is generally done by the researcher himself. An audit can be a store audit or a pantry audit. The store audits are performed by the distributors or manufacturers in order to ana1yse the market share, purchase pattern etc. e.g. the researcher may check the store records or do an analysis of inventory on hand to record the data. The pantry audit involves the researcher developing an inventory of brands quantities and package sizes of products in a consumer”s home, generally in the course of a personal interview. Such an audit is used to supplement or test the truthfulness of information provided in the direct questionnaire. Content
Statistics – Geometric Probability Distribution ”; Previous Next The geometric distribution is a special case of the negative binomial distribution. It deals with the number of trials required for a single success. Thus, the geometric distribution is a negative binomial distribution where the number of successes (r) is equal to 1. Formula ${P(X=x) = p times q^{x-1} }$ Where − ${p}$ = probability of success for single trial. ${q}$ = probability of failure for a single trial (1-p) ${x}$ = the number of failures before a success. ${P(X-x)}$ = Probability of x successes in n trials. Example Problem Statement: In an amusement fair, a competitor is entitled for a prize if he throws a ring on a peg from a certain distance. It is observed that only 30% of the competitors are able to do this. If someone is given 5 chances, what is the probability of his winning the prize when he has already missed 4 chances? Solution: If someone has already missed four chances and has to win in the fifth chance, then it is a probability experiment of getting the first success in 5 trials. The problem statement also suggests the probability distribution to be geometric. The probability of success is given by the geometric distribution formula: ${P(X=x) = p times q^{x-1} }$ Where − ${p = 30 % = 0.3 }$ ${x = 5}$ = the number of failures before a success. Therefore, the required probability: $ {P(X=5) = 0.3 times (1-0.3)^{5-1} , \[7pt] , = 0.3 times (0.7)^4, \[7pt] , approx 0.072 \[7pt] , approx 7.2 % }$ Print Page Previous Next Advertisements ”;
Factorial
Statistics – Factorial ”; Previous Next Factorial is a function applied to natural numbers greater than zero. The symbol for the factorial function is an exclamation mark after a number, like this: 2! Formula ${n! = 1 times 2 times 3 … times n}$ Where − ${n!}$ = represents factorial ${n}$ = Number of sets Example Problem Statement: Calculate the factorial of 5 i.e. 5!. Solution: Multiply all the whole numbers up to the number considered. ${5! = 5 times 4 times 3 times 2 times 1 , \[7pt] , = 120}$ Print Page Previous Next Advertisements ”;
Statistics Notation
Statistics – Notations ”; Previous Next Following table shows the usage of various symbols used in Statistics Capitalization Generally lower case letters represent the sample attributes and capital case letters are used to represent population attributes. $ P $ – population proportion. $ p $ – sample proportion. $ X $ – set of population elements. $ x $ – set of sample elements. $ N $ – set of population size. $ N $ – set of sample size. Greek Vs Roman letters Roman letters represent the sample attributs and greek letters are used to represent Population attributes. $ mu $ – population mean. $ bar x $ – sample mean. $ delta $ – standard deviation of a population. $ s $ – standard deviation of a sample. Population specific Parameters Following symbols represent population specific attributes. $ mu $ – population mean. $ delta $ – standard deviation of a population. $ {mu}^2 $ – variance of a population. $ P $ – proportion of population elements having a particular attribute. $ Q $ – proportion of population elements having no particular attribute. $ rho $ – population correlation coefficient based on all of the elements from a population. $ N $ – number of elements in a population. Sample specific Parameters Following symbols represent population specific attributes. $ bar x $ – sample mean. $ s $ – standard deviation of a sample. $ {s}^2 $ – variance of a sample. $ p $ – proportion of sample elements having a particular attribute. $ q $ – proportion of sample elements having no particular attribute. $ r $ – population correlation coefficient based on all of the elements from a sample. $ n $ – number of elements in a sample. Linear Regression $ B_0 $ – intercept constant in a population regression line. $ B_1 $ – regression coefficient in a population regression line. $ {R}^2 $ – coefficient of determination. $ b_0 $ – intercept constant in a sample regression line. $ b_1 $ – regression coefficient in a sample regression line. $ ^{s}b_1 $ – standard error of the slope of a regression line. Probability $ P(A) $ – probability that event A will occur. $ P(A|B) $ – conditional probability that event A occurs, given that event B has occurred. $ P(A”) $ – probability of the complement of event A. $ P(A cap B) $ – probability of the intersection of events A and B. $ P(A cup B) $ – probability of the union of events A and B. $ E(X) $ – expected value of random variable X. $ b(x; n, P) $ – binomial probability. $ b*(x; n, P) $ – negative binomial probability. $ g(x; P) $ – geometric probability. $ h(x; N, n, k) $ – hypergeometric probability. Permutation/Combination $ n! $ – factorial value of n. $ ^{n}P_r $ – number of permutations of n things taken r at a time. $ ^{n}C_r $ – number of combinations of n things taken r at a time. Set $ A Cap B $ – intersection of set A and B. $ A Cup B $ – union of set A and B. $ { A, B, C } $ – set of elements consisting of A, B, and C. $ emptyset $ – null or empty set. Hypothesis Testing $ H_0 $ – null hypothesis. $ H_1 $ – alternative hypothesis. $ alpha $ – significance level. $ beta $ – probability of committing a Type II error. Random Variables $ Z $ or $ z $ – standardized score, also known as a z score. $ z_{alpha} $ – standardized score that has a cumulative probability equal to $ 1 – alpha $. $ t_{alpha} $ – t statistic that has a cumulative probability equal to $ 1 – alpha $. $ f_{alpha} $ – f statistic that has a cumulative probability equal to $ 1 – alpha $. $ f_{alpha}(v_1, v_2) $ – f statistic that has a cumulative probability equal to $ 1 – alpha $ and $ v_1 $ and $ v_2 $ degrees of freedom. $ X^2 $ – chi-square statistic. Summation Symbols $ sum $ – summation symbol, used to compute sums over a range of values. $ sum x $ or $ sum x_i $ – sum of a set of n observations. Thus, $ sum x = x_1 + x_2 + … + x_n $. Print Page Previous Next Advertisements ”;
Tableau – Basic Filters
Tableau – Basic Filters ”; Previous Next Filtering is the process of removing certain values or range of values from a result set. Tableau filtering feature allows both simple scenarios using field values as well as advanced calculation or context-based filters. In this chapter, you will learn about the basic filters available in Tableau. There are three types of basic filters available in Tableau. They are as follows − Filter Dimensions are the filters applied on the dimension fields. Filter Measures are the filters applied on the measure fields. Filter Dates are the filters applied on the date fields. Filter Dimensions These filters are applied on the dimension fields. Typical examples include filtering based on categories of text or numeric values with logical expressions greater than or less than conditions. Example We use the Sample – Superstore data source to apply dimension filters on the sub-category of products. We create a view for showing profit for each sub-category of products according to their shipping mode. For it, drag the dimension field “Sub-Category” to the Rows shelf and the measure field “profit” to the Columns shelf. Next, drag the Sub-Category dimension to the Filters shelf to open the Filter dialog box. Click the None button at the bottom of the list to deselect all segments. Then, select the Exclude option in the lower right corner of the dialog box. Finally, select Labels and Storage and then click OK. The following screenshot shows the result with the above two categories excluded. Filter Measures These filters are applied on the measure fields. Filtering is based on the calculations applied to the measure fields. Hence, while in dimension filters you use only values to filter, in measures filter you use calculations based on fields. Example You can use the Sample – Superstore data source to apply dimension filters on the average value of the profits. First, create a view with ship mode and subcategory as dimensions and Average of profit as shown in the following screenshot. Next, drag the AVG (profit) value to the filter pane. Choose Average as the filter mode. Next, choose “At least” and give a value to filter the rows, which meet these criteria. After completion of the above steps, we get the final view below showing only the subcategories whose average profit is greater than 20. Filter Dates Tableau treats the date field in three different ways while applying the date field. It can apply filter by taking a relative date as compared to today, an absolute date, or range of dates. Each of this option is presented when a date field is dragged out of the filter pane. Example We choose the sample – Superstore data source and create a view with order date in the column shelf and profit in the rows shelf as shown in the following screenshot. Next, drag the “order date” field to the filter shelf and choose Range of dates in the filter dialog box. Choose the dates as shown in the following screenshot. On clicking OK, the final view appears showing the result for the chosen range of dates as seen in the following screenshot. Print Page Previous Next Advertisements ”;
Z table
Statistics – Z table ”; Previous Next Standard Normal Probability Table The following table shows the area under the curve to the left of a z-score: z .00 .01 .02 .03 .04 .05 .06 .07 .08 .09 -3.4 .0003 .0003 .0003 .0003 .0003 .0003 .0003 .0003 .0003 .0002 -3.3 .0005 .0005 .0005 .0004 .0004 .0004 .0004 .0004 .0004 .0003 -3.2 .0007 .0007 .0006 .0006 .0006 .0006 .0006 .0005 .0005 .0005 -3.1 .0010 .0009 .0009 .0009 .0008 .0008 .0008 .0008 .0007 .0007 -3.0 .0013 .0013 .0013 .0012 .0012 .0011 .0011 .0011 .0010 .0010 -2.9 .0019 .0018 .0018 .0017 .0016 .0016 .0015 .0015 .0014 .0014 -2.8 .0026 .0025 .0024 .0023 .0023 .0022 .0021 .0021 .0020 .0019 -2.7 .0035 .0034 .0033 .0032 .0031 .0030 .0029 .0028 .0027 .0026 -2.6 .0047 .0045 .0044 .0043 .0041 .0040 .0039 .0038 .0037 .0036 -2.5 .0062 .0060 .0059 .0057 .0055 .0054 .0052 .0051 .0049 .0048 -2.4 .0082 .0080 .0078 .0075 .0073 .0071 .0069 .0068 .0066 .0064 -2.3 .0107 .0104 .0102 .0099 .0096 .0094 .0091 .0089 .0087 .0084 -2.2 .0139 .0136 .0132 .0129 .0125 .0122 .0119 .0116 .0113 .0110 -2.1 .0179 .0174 .0170 .0166 .0162 .0158 .0154 .0150 .0146 .0143 -2.0 .0228 .0222 .0217 .0212 .0207 .0202 .0197 .0192 .0188 .0183 -1.9 .0287 .0281 .0274 .0268 .0262 .0256 .0250 .0244 .0239 .0233 -1.8 .0359 .0351 .0344 .0336 .0329 .0322 .0314 .0307 .0301 .0294 -1.7 .0446 .0436 .0427 .0418 .0409 .0401 .0392 .0384 .0375 .0367 -1.6 .0548 .0537 .0526 .0516 .0505 .0495 .0485 .0475 .0465 .0455 -1.5 .0668 .0655 .0643 .0630 .0618 .0606 .0594 .0582 .0571 .0559 -1.4 .0808 .0793 .0778 .0764 .0749 .0735 .0721 .0708 .0694 .0681 -1.3 .0968 .0951 .0934 .0918 .0901 .0885 .0869 .0853 .0838 .0823 -1.2 .1151 .1131 .1112 .1093 .1075 .1056 .1038 .1020 .1003 .0985 -1.1 .1357 .1335 .1314 .1292 .1271 .1251 .1230 .1210 .1190 .1170 -1.0 .1587 .1562 .1539 .1515 .1492 .1469 .1446 .1423 .1401 .1379 -0.9 .1841 .1814 .1788 .1762 .1736 .1711 .1685 .1660 .1635 .1611 -0.8 .2119 .2090 .2061 .2033 .2005 .1977 .1949 .1922 .1894 .1867 -0.7 .2420 .2389 .2358 .2327 .2296 .2266 .2236 .2206 .2177 .2148 -0.6 .2743 .2709 .2676 .2643 .2611 .2578 .2546 .2514 .2483 .2451 -0.5 .3085 .3050 .3015 .2s981 .2946 .2912 .2877 .2843 .2810 .2776 -0.4 .3446 .3409 .3372 .3336 .3300 .3264 .3228 .3192 .3156 .3121 -0.3 .3821 .3783 .3745 .3707 .3669 .3632 .3594 .3557 .3520 .3483 -0.2 .4207 .4168 .4129 .4090 .4052 .4013 .3974 .3936 .3897 .3859 -0.1 .4602 .4562 .4522 .4483 .4443 .4404 .4364 .4325 .4286 .4247 0.0 .5000 .4960 .4920 .4880 .4840 .4801 .4761 .4721 .4681 .4641 The following table shows the area under the curve to the left of a z-score: z .00 .01 .02 .03 .04 .05 .06 .07 .08 .09 0.0 .5000 .4960 .4920 .4880 .4840 .4801 .4761 .4721 .4681 .4641 0.1 .5398 .5438 .5478 .5517 .5557 .5596 .5636 .5675 .5714 .5753 0.2 .5793 .5832 .5871 .5910 .5948 .5987 .6026 .6064 .6103 .6141 0.3 .6179 .6217 .6255 .6293 .6331 .6368 .6406 .6443 .6480 .6517 0.4 .6554 .6591 .6628 .6664 .6700 .6736 .6772 .6808 .6844 .6879 0.5 .6915 .6950 .6985 .7019 .7054 .7088 .7123 .7157 .7190 .7224 0.6 .7257 .7291 .7324 .7357 .7389 .7422 .7454 .7486 .7517 .7549 0.7 .7580 .7611 .7642 .7673 .7704 .7734 .7764 .7794 .7823 .7852 0.8 .7881 .7910 .7939 .7967 .7995 .8023 .8051 .8078 .8106 .8133 0.9 .8159 .8186 .8212 .8238 .8264 .8289 .8315 .8340 .8365 .8389 1.0 .8413 .8438 .8461 .8485 .8508 .8531 .8554 .8577 .8599 .8621 1.1 .8643 .8665 .8686 .8708 .8729 .8749 .8770 .8790 .8810 .8830 1.2 .8849 .8869 .8888 .8907 .8925 .8944 .8962 .8980 .8997 .9015 1.3 .9032 .9049 .9066 .9082 .9099 .9115 .9131 .9147 .9162 .9177 1.4 .9192 .9207 .9222 .9236 .9251 .9265 .9279 .9292 .9306 .9319 1.5 .9332 .9345 .9357 .9370 .9382 .9394 .9406 .9418 .9429 .9441 1.6 .9452 .9463 .9474 .9484 .9495 .9505 .9515 .9525 .9535 .9545 1.7 .9554 .9564 .9573 .9582 .9591 .9599 .9608 .9616 .9625 .9633 1.8 .9641 .9649 .9656 .9664 .9671 .9678 .9686 .9693 .9699 .9706 1.9 .9713 .9719 .9726 .9732 .9738 .9744 .9750 .9756 .9761 .9767 2.0 .9772 .9778 .9783 .9788 .9793 .9798 .9803 .9808 .9812 .9817 2.1 .9821 .9826 .9830 .9834 .9838 .9842 .9846 .9850 .9854 .9857 2.2 .9861 .9864 .9868 .9871 .9875 .9878 .9881 .9884 .9887 .9890 2.3 .9893 .9896 .9898 .9901 .9904 .9906 .9909 .9911 .9913 .9916 2.4 .9918 .9920 .9922 .9925 .9927 .9929 .9931 .9932 .9934 .9936 2.5 .9938 .9940 .9941 .9943 .9945 .9946 .9948 .9949 .9951 .9952 2.6 .9953 .9955 .9956 .9957 .9959 .9960 .9961 .9962 .9963 .9964 2.7 .9965 .9966 .9967 .9968 .9969 .9970 .9971 .9972 .9973 .9974 2.8 .9974 .9975 .9976 .9977 .9977 .9978 .9979 .9979 .9980 .9981 2.9 .9981 .9982 .9982 .9983 .9984 .9984 .9985 .9985 .9986 .9986 3.0 .9987 .9987 .9987 .9988 .9988 .9989 .9989 .9989 .9990 .9990 3.1 .9990 .9991 .9991 .9991 .9992 .9992 .9992 .9992 .9993 .9993 3.2 .9993 .9993 .9994 .9994 .9994 .9994 .9994 .9995 .9995 .9995 3.3 .9995 .9995 .9995 .9996 .9996 .9996 .9996 .9996 .9996 .9997 3.4 .9997 .9997 .9997 .9997 .9997 .9997 .9997 .9997 .9997 .9998 Print Page Previous Next Advertisements ”;
Correlation Co-efficient
Statistics – Correlation Co-efficient ”; Previous Next Correlation Co-efficient A correlation coefficient is a statistical measure of the degree to which changes to the value of one variable predict change to the value of another. In positively correlated variables, the value increases or decreases in tandem. In negatively correlated variables, the value of one increases as the value of the other decreases. Correlation coefficients are expressed as values between +1 and -1. A coefficient of +1 indicates a perfect positive correlation: A change in the value of one variable will predict a change in the same direction in the second variable. A coefficient of -1 indicates a perfect negative: A change in the value of one variable predicts a change in the opposite direction in the second variable. Lesser degrees of correlation are expressed as non-zero decimals. A coefficient of zero indicates there is no discernable relationship between fluctuations of the variables. Formula ${r = frac{N sum xy – (sum x)(sum y)}{sqrt{[Nsum x^2 – (sum x)^2][Nsum y^2 – (sum y)^2]}} }$ Where − ${N}$ = Number of pairs of scores ${sum xy}$ = Sum of products of paired scores. ${sum x}$ = Sum of x scores. ${sum y}$ = Sum of y scores. ${sum x^2}$ = Sum of squared x scores. ${sum y^2}$ = Sum of squared y scores. Example Problem Statement: Calculate the correlation co-efficient of the following: X Y 1 2 3 5 4 5 4 8 Solution: ${ sum xy = (1)(2) + (3)(5) + (4)(5) + (4)(8) = 69 \[7pt] sum x = 1 + 3 + 4 + 4 = 12 \[7pt] sum y = 2 + 5 + 5 + 8 = 20 \[7pt] sum x^2 = 1^2 + 3^2 + 4^2 + 4^2 = 42 \[7pt] sum y^2 = 2^2 + 5^2 + 5^2 + 8^2 = 118 \[7pt] r= frac{69 – frac{(12)(20)}{4}}{sqrt{(42 – frac{(12)^2}{4})(118-frac{(20)^2}{4}}} \[7pt] = .866 }$ Print Page Previous Next Advertisements ”;
Tableau – Filter Operations
Tableau – Filter Operations ”; Previous Next Any data analysis and visualization work involves the use of extensive filtering of data. Tableau has a very wide variety of filtering options to address these needs. There are many inbuilt functions for applying filters on the records using both dimensions and measures. The filter option for measures offers numeric calculations and comparison. The filter option for dimension offers choosing string values from a list or using a custom list of values. In this chapter, you will learn about the various options as well as the steps to edit and clear the filters. Creating Filters Filters are created by dragging the required field to the Filters shelf located above the Marks card. Create a horizontal bar chart by dragging the measure sales to the Columns shelf and the dimension Sub-Category to the Rows shelf. Again drag the measure sales into the Filters shelf. Once this filter is created, right-click and choose the edit filter option from the pop-up menu. Creating Filters for Measures Measures are numeric fields. So, the filter options for such fields involve choosing values. Tableau offers the following types of filters for measures. Range of Values − Specifies the minimum and maximum values of the range to include in the view. At Least − Includes all values that are greater than or equal to a specified minimum value. At Most − Includes all values that are less than or equal to a specified maximum value. Special − Helps you filter on Null values. Include only Null values, Non-null values, or All Values. Following worksheet shows these options. Creating Filters for Dimensions Dimensions are descriptive fields having values which are strings. Tableau offers the following types of filters for dimensions. General Filter − allows to select specific values from a list. Wildcard Filter − allows to mention wildcards like cha* to filter all string values starting with cha. Condition Filter − applies conditions such as sum of sales. Top Filter − chooses the records representing a range of top values. Following worksheet shows these options. Clearing Filters Filters can be easily removed by choosing the clear filter option as shown in the following screenshot. Print Page Previous Next Advertisements ”;