62 results about "Objective variables" patented technology

A linear regression equation is comprised of the objective variables and the explanatory variables. The objective variables are comprised of the L1 carrier phases, the L2 carrier phases, the C / A code pseudorange, the P(Y) code pseudorange, the clock error data, the delay data of the ionosphere, and the delay data of the troposphere that related with every satellite respectively. The explanatory variables are comprised of at least the integer ambiguity and a receiver's position.The receiver's position has been linear-approximated by using the estimation results of the passed receiver's positions. Applying a least square method to the regression equation, the integer ambiguity and the receiver's position is estimated.

The invention provides a visualization method for multivariable spatio-temporal data under a polar region projection mode. The visualization method comprises the following steps of: obtaining an objective variable and geographic region data; drawing an objective variable visualization image according to the objective variable; drawing a geographic region background according to geographic region data; and displaying an effect image according to the objective variable visualization image and the geographic region background. According to the method of the embodiment of the invention, three polar region projection conversion methods from a two-dimensional orthogonal longitude and latitude space to a two-dimensional polar region projection space are realized, a uniform vector representation mode and a drawing method in the two-dimensional orthogonal longitude and latitude space and the two-dimensional polar region projection space are supported, and auxiliary positioning information of the geographic region background is provided, therefore, the problem that polar regions in the two-dimensional orthogonal longitude and latitude space are discontinuous is overcome for a user, and the distribution situations of variables in polar regions can be observed and compared efficiently and interactively in real time.

Abnormal values of an objective variable are removed. A degree of association between an objective variable and a plurality of explanatory variables is calculated. A plurality of explanatory variables having a high degree of association are extracted. A degree of independence between the explanatory variables is calculated. A plurality of candidates of explanatory variables with a high possibility of having a great effect on the objective variable are selected based on the degree of association and the degree of independence. An explanatory variable having a high rate of contribution to the objective variable is selected from among the candidates, based on the cumulative contribution rate, and a regression equation is calculated to estimate a value of the objective variable. The same processing is repeated, using the difference as a new objective variable and explanatory variables except the explanatory variable used to obtain the difference as new explanatory variables.

It is an object of the present invention to improve analysis accuracy of manufacture data with missing values. First, an analysis target explanatory variable is selected. The manufacture data acquired from each process is separated into a set with missing values and a set without missing values. An evaluation value for evaluating the influence of the explanatory variable on the objective variable is calculated from the manufacture data in the set with missing value and the set without missing values. The manufacture data is analyzed by matching the calculated evaluation value with a reference.

An article residual value predicting device of the invention comprises an article residual value predicting computer, a first data memory device connected to the article residual value predicting computer to store, as basal record data, respective items such as article names, used article values for each article type, new article values for each article type, and year and month data to which the used article value is applied, a second data memory device connected to the article residual value predicting computer to store item category scores. The article residual value predicting computer comprises article residual rate proven-value calculating means for reading out the used article value and new article value for each article type stored in the first data memory device, calculating article residual rate proven-value from the ratio of the used article value to the new article value, and storing a calculated result thus obtained as an article residual rate proven-value in the first data memory device, category score calculating means for reading out the article name, article residual rate proven-value, year data to which the used article value is applied and month data to which the used article value is applied, which are stored in the first data memory device, and calculating an item category score by performing a regression analysis based on the qualification theory I using the readout article residual rate proven-value as an objective variable and the readout article name, the year to which the used article value is applied as an explanatory variable and the month to which the used article value is applied as an explanatory variable, and storing a calculated score thus obtained in the second data memory device, article residual rate predictive-value calculating means for reading out the score stored in the second data memory device with respect to a specified item category and adopting a year-classified score relative to the year at some future point to be predicted as the year-classified score to calculate an article residual rate predictive-value from an equation “(article residual rate predictive-value)=(item-classified score)+(year-classified score)+(month-classified score)+(constant value)”, and article residual rate calculating means for multiplying the article residual rate predictive-value by a new article value to calculate an article residual value. The first data memory device serves to store maker-classified new article sales quantity or article name-classified new article sales quantity before elapsed years. The article residual value predicting computer further comprises a first weight coefficient calculating means for reading out the maker-classified new article sales quantity or article name-classified new article sales quantity before elapsed years stored in the first data memory device, calculating a weight coefficient from an equation “(maker-classified new article sales quantity before elapsed years) / (maker-classified record number)” or “(article name-classified new article sales quantity before elapsed years) / (article name-classified record number)”, and storing the weight coefficient based on the calculated new article sales quantity in the first data memory device, and weighting means for reading out the weight coefficient based on the calculated new article sales quantity from the first data memory device and duplicating the number of relevant records stored in the first data memory device corresponding to the weight coefficient based on the readout new article sales quantity and storing the record numbers increased by duplicating. The category score calculating means serves to perform the aforementioned regression analysis using concurrently all the relevant records weighted by the weighting means collectively.

The analysis method includes steps of acquiring measurement data obtained from swings and values of hitting results (flight distance or the like) (STEP 3); calculating a characteristic value (face angle or the like) from the measurement data (STEP 4); and determining an indicator for selecting a shaft of a golf club from the characteristic value and the values of hitting results (STEP 5). In the step of determining an indicator, when a hitting result of a golf club is an objective variable, and the characteristic value is an explanatory variable together with a predetermined shaft physical property (flex point or the like), and has a statistically significant relation with the hitting result, the characteristic value is determined as the indicator. A relational expression of the indicator and the hitting result is calculated for each value of the physical properties.

A linear regression equation is comprised of the objective variables and the explanatory variables. The objective variables are comprised of the L1 carrier phases, the L2 carrier phases, the C / A code pseudorange, the P(Y) code pseudorange, the clock error data, the delay data of the ionosphere, and the delay data of the troposphere that related with every satellite respectively. The explanatory variables are comprised of at least the integer ambiguity and a receiver's position.The receiver's position has been linear-approximated by using the estimation results of the passed receiver's positions. Applying a least square method to the regression equation, the integer ambiguity and the receiver's position is estimated.

A failure prevention diagnosis support system includes: an acquiring portion that acquires internal information about an internal state of an image forming apparatus; a storage portion that stores one or a plurality of logistic regression models that define an estimate value of a regression coefficient through a logistic regression analysis using the internal information obtained when the image forming apparatus is in a failed state and in a normal state; and a controller that performs a control operation to select a logistic regression model from the one or the plurality of the logistic regression models stored in the storage portion in accordance with the image forming apparatus, and to calculate risk degrees as objective variables that are indicators of failure degrees in the image forming apparatus by assigning the internal information acquired by the acquiring portion or the value obtained from the internal information to the selected logistic regression model.

A data analysis support systems according to the present invention assumes any of multiple indices to be an objective variable, implements clustering and collectively outputs indices belonging to the identical cluster.

The present invention has been made to calculate objective variable distribution having high reliability irrespective of the section size in an explanatory variable in which frequency drastically changes to thereby obtain highly reliable importance degree. An importance degree calculation program comprises: a section generation step that receives, as an input, an instance set and an explanatory variable and uses the instance set to divide the explanatory variable into a plurality of sections to obtain a section set; a neighborhood instance set extraction step that uses the instance set, the section set, and a neighborhood instance number threshold to extract from across all sections a neighborhood instance set of each section in which the number of instances is greater than the neighborhood instance number threshold; an objective variable distribution calculation step that calculates an objective variable distribution from the neighborhood instance set of each section extracted by the neighborhood instance set extraction step; and an importance degree calculation step that calculates importance degree of each section from the objective variable distribution in each section obtained by the objective variable distribution calculation step and instance set.

A computer-readable recording medium stores therein a scale-parameter calculating program. The scale-parameter calculating program causes a computer to implement a method of calculating scale parameters, each of which is for use in normalization of a distribution of values of each of explanatory variables in calculation of inter-explanatory variable distances, to predict a value of a objective variable of an unknown case by extracting one or more known cases similar to the unknown case from known cases based on inter-case distances. The inter-case distances is obtained based on the inter-explanatory distances, each of the inter-explanatory variable distances is a distance between each of the known cases and the unknown case taken for each of explanatory variables, each of the cases includes the explanatory variables each expressed by a numerical value and the objective variable expressed by a character string. Each of the known cases is a case that has a known objective variable value, the unknown case is a case that has an unknown objective variable value.