Siemens Healthineers Ag Stock Statistic Functions Linear Regression

SMMNY Stock  USD 26.83  0.26  0.98%   
Siemens Healthineers statistic functions tool provides the execution environment for running the Linear Regression function and other technical functions against Siemens Healthineers. Siemens Healthineers value trend is the prevailing direction of the price over some defined period of time. The concept of trend is an important idea in technical analysis, including the analysis of statistic functions indicators. As with most other technical indicators, the Linear Regression function function is designed to identify and follow existing trends. Siemens Healthineers statistical functions help analysts to determine different price movement patterns based on how price series statistical indicators change over time. Please specify Time Period to run this model.

Execute Function
The output start index for this execution was thirteen with a total number of output elements of fourty-eight. The Linear Regression model generates relationship between price series of Siemens Healthineers and its peer or benchmark and helps predict Siemens Healthineers future price from its past values.

Siemens Healthineers Technical Analysis Modules

Most technical analysis of Siemens Healthineers help investors determine whether a current trend will continue and, if not, when it will shift. We provide a combination of tools to recognize potential entry and exit points for Siemens from various momentum indicators to cycle indicators. When you analyze Siemens charts, please remember that the event formation may indicate an entry point for a short seller, and look at other indicators across different periods to confirm that a breakdown or reversion is likely to occur.

About Siemens Healthineers Predictive Technical Analysis

Predictive technical analysis modules help investors to analyze different prices and returns patterns as well as diagnose historical swings to determine the real value of Siemens Healthineers AG. We use our internally-developed statistical techniques to arrive at the intrinsic value of Siemens Healthineers AG based on widely used predictive technical indicators. In general, we focus on analyzing Siemens Pink Sheet price patterns and their correlations with different microeconomic environment and drivers. We also apply predictive analytics to build Siemens Healthineers's daily price indicators and compare them against related drivers, such as statistic functions and various other types of predictive indicators. Using this methodology combined with a more conventional technical analysis and fundamental analysis, we attempt to find the most accurate representation of Siemens Healthineers's intrinsic value. In addition to deriving basic predictive indicators for Siemens Healthineers, we also check how macroeconomic factors affect Siemens Healthineers price patterns. Please read more on our technical analysis page or use our predictive modules below to complement your research.
Hype
Prediction
LowEstimatedHigh
25.2226.8328.44
Details
Intrinsic
Valuation
LowRealHigh
25.7427.3528.96
Details
Naive
Forecast
LowNextHigh
24.5926.2027.81
Details
Bollinger
Band Projection (param)
LowerMiddle BandUpper
25.3026.4927.68
Details

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Additional Tools for Siemens Pink Sheet Analysis

When running Siemens Healthineers' price analysis, check to measure Siemens Healthineers' market volatility, profitability, liquidity, solvency, efficiency, growth potential, financial leverage, and other vital indicators. We have many different tools that can be utilized to determine how healthy Siemens Healthineers is operating at the current time. Most of Siemens Healthineers' value examination focuses on studying past and present price action to predict the probability of Siemens Healthineers' future price movements. You can analyze the entity against its peers and the financial market as a whole to determine factors that move Siemens Healthineers' price. Additionally, you may evaluate how the addition of Siemens Healthineers to your portfolios can decrease your overall portfolio volatility.